text:
Professionalism/PIP and Silopren

Background
Poly Implant Prothese (PIP) was a French company founded by Jean-Claude Mas in 1991. PIP is infamous for their silicone gel breast implants which were sold primarily to European, Latin American, and Australian women.

Timeline
In 1991, PIP received French health ministry approval for their silicone gel breast implant. Their silicone was required to be purchased from an external, medical grade silicone manufacturer.

In 2001, PIP illegally switched their formula from the approved externally sourced medical grade silicone to an in-house produced industrial grade silicone in the majority of its implants. This new industrial formula was around 90% cheaper and contained chemicals such as silopren, baysilone, and rhodorsil which are normally used as fuel additives or in rubber tubing. While silopren toxicology effects have not yet been determined, its safety data sheet recommends proper handling with protective equipment and immediate contacting of a poison treatment specialist if large qualities are ingested or inhaled.

In 2009, surgeons reported high rupture rates for the PIP silicone implants, in which the implant tears and the silicone gel can leak into the body. This can cause lumps, inflammation, and tissue hardening. In this case, the unapproved silicone was brought into direct bodily contact. PIP was liquidated shortly after.

In 2011, the French health ministry conducted an investigation and concluded that there is no link between the implants and breast cancer. However, 20 cases of breast cancer were documented in women with the implants, including one death in which the cancer formed directly from the scar tissue surrounding the implant.

In 2013, Jean-Claude Mas was convicted for fraud, sentenced to prison for four years, and fined 75,000 euros. Four other PIP executives were convicted with lesser sentences ranging from 1-3 years. Around 300,000 women are thought to be impacted by the faulty implants.

Jean-Claude Mas
Mas claims to have "organized everything to escape being monitored" and that "we did it for 13 years without a problem". He allegedly told police he had ordered staff to "hide the truth" in 1993. While PIP was thought to make the switch in their formula in 2001, these statements indicate that the corruption and intentional deception of regulatory agencies may have gone on for longer. Yves Haddad, the lawyer representing PIP and Mas, blatantly states that PIP "did not formally receive the regulators' approval" and there was "a violation of regulations". When asked about the company reasoning behind their non-approved product, Haddad stated that is was "because of corporate management that tried to get the best cost". Most evidence points to corporate management, and specifically Mas, being the mastermind behind the fraudulent operation.

The Engineers
While Mas may have been the mastermind, the engineers were not completely ethical in their work. According to trial attendee Isabel Traeger, when they asked one engineer how they made their implants, he responded with "you use your best guest". This demonstrates not only poor manufacturing practices, but also reveals that the engineers knew they were not complying with medical regulations. However, former worker Eric Mariaccia claims that “the responsible ones aren’t the workers but the heads of the company" . However, the workers did not comply with regulations nor conduct any pre-launch clinical trials as required by their profession.

The FDA
It is worth noting that PIP's faulty breast implants did not affect women in the United States. The reason being that in 2000, the FDA did not approve PIP's saline implant, which differs from the silicone gel implants that caused the issues in other countries. At the time, silicone gel breast implants were banned throughout the US. Therefore, PIP's only way to sell their products in American markets was through the new saline implant. The FDA investigated PIP's manufacturing plant and sent a warning letter to the company's owner, Mas, citing 11 deviations from good manufacturing processes. These deviations are related to the saline implants, which are a different line from the silicone implants. However, the plant inspected produced both types. While the FDA claimed they made this warning letter public, French authorities claim they were not informed directly. It is unknown if the FDA shared this information with France in 2000. This lack of communication is thought to have damaged PIP's anticipated revenue and potentially contributed to the company switching formulas. It is impossible to know what would have happened if France had this information in 2000.

TÜV Rheinland
In 1997, PIP commissioned TÜV Rheinland as a Notified Body to evaluate its quality assurance system. Notified Bodies assist medical device manufacturers, such as PIP, in complying with regulations and standards and implementing proper quality assurance systems. However, manufacturers remain solely responsible for the safety and quality of their products. TÜV Rheinland claimed they “acted responsibly and in compliance with all laws and regulations at any time – from the initial assessment of PIP’s quality assurance system, the examination of the design dossiers to subsequent audits… [and] Where deviations were found these were diligently followed up. At no time there was an indication of criminal or unlawful behavior by PIP”. TÜV Rheinland also stated that if PIP had manufactured its products in accordance with the reviewed design dossier and quality assurance processes, the implants would have complied with all legal requirements.

TÜV Rheinland issued a press release stating “ PIP deceived all parties involved – most importantly the patients, but also the competent supervising authorities and TÜV Rheinland”. PIP kept complete records of the use of approved implant materials and concealed any hints of different raw materials. When PIP's fraud was exposed, TÜV Rheinland immediately suspended the certificates. Despite TÜV Rheinland's defense, a French court ruled in 2021 that TÜV Rheinland had been negligent in awarding safety certificates for PIP implants. Various court orders required TÜV Rheinland to pay compensation to faulty breast implant victims.

In conclusion, TÜV Rheinland's involvement in PIP's fraudulent activity resulted in legal consequences. Despite their claim that they acted responsibly and in compliance with all laws and regulations, a French court found them negligent in awarding safety certificates for PIP implants. The court ordered TÜV Rheinland to pay compensation to victims of faulty breast implants.

Corporate Leaders and Ethical Decision-Making
CEOs and other company leaders have a duty to ethical decision-making since they play a crucial role in establishing the general tone and direction of their organizations. They should act in the best interest of their company, which includes not only customers, employees, and shareholders, but also the wider community. During Jean-Claude Mas' tenure as CEO, he made a number of unethical decisions that put the health and safety of thousands of women at risk. It was Mas's plan to shift from externally sourced medical-grade silicone to internally produced industrial-grade silicone, knowing this would make the implants significantly less safe. PIP would have continued to sell the cheap implants had they not been caught in 2011. As CEO, Mas had the responsibility to ensure that his company's product was of high quality and designed to minimize risk of harm to patients. However, he failed to do so and many women suffered because of his selfish decisions. Even after being caught, Mas showed no remorse to those affected and denied any wrongdoing.

The Ring of Gyges and Selfish CEOs
The story of the Ring of Gyges can be used to explain the conduct of self-serving CEOs. In the story, a shepherd discovers a powerful ring that makes him invisible and subsequently leads him to become increasingly corrupt and selfish, committing immoral acts without fear of consequence. The same way that the shepherd was corrupted by the power of the ring, some CEOs may become corrupted by the power of their position. When granted enormous power and wealth, these CEOs may become selfish and corrupt, as they feel invincible and above the law. This can lead them to make unethical decisions, such as engaging in fraudulent behavior, exploiting workers, or prioritizing short-term profits over long-term sustainability. In Mas's case, he pushed production of unsafe, cheap implants, ordered employees to cover up evidence, falsified paperwork, and ignored medical regulations. The lack of oversight and accountability can create a sense of impunity that encourages such behavior. This story is a cautionary tale about the corrupting influence of power and highlights the important of ethical leadership.

Leadership Virtues, Employee Ethics, and Reporting Wrongdoing
Another connection can be made between PIP and The Great Learning excerpt by Confucius. “When the self is cultivated, the family is in harmony. When the families are in harmony, the state is well governed. When the state is well governed, tranquility reins throughout the land.” However if the root is in disorder, then there will be disorder throughout the rest of the steps. This stresses the need for leaders to prioritize personal development and cultivate a virtuous character in order to govern justly and effectively. It’s clear that the leader and root in this case, CEO Jean-Claude Mas, did not do any of that, which lead to a state of disorder. Due to one man and other company leaders with the same mindset, thousands of peoples lives were negatively affected. Regulatory bodies like TUV Rheinland were criticized for negligence, doctors and healthcare professionals who recommended or used PIP implants had their reputations affected, and most importantly the patients who received the faulty breast implants, many experienced complications such as ruptured or leaking implants, inflammation, pain and even death.

Employees also have an ethical duty to report any wrongdoings or injustices within the company when they are made aware of it. PIP Employees who were complicit and agreed to cover up evidence were also at fault. Had it not been for the doctors who began reporting higher than usual rupture rates, then there wouldn’t have been investigations launched that led to the discovery of the company’s decade long fraud and corruption. No matter the position or role, employees should attempt to speak up against wrongdoing and try to bring awareness to it. Even if they are fired, it is not their fault, it is likely that the company’s foundations and virtues are flawed. A job can always be replaced, but a life lost can never be regained.

title: Professionalism/PIP and Silopren


text: Category:Meringue recipes

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

title: Category:Meringue recipes


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Talk:Time Travel Claims and Urban Legends

How do you not make a page be in Query?
I need help its my first time but do you know how to get rid of a page in the Query 71.163.46.63 (discuss) 01:00, 31 March 2023 (UTC)


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Wikibooks:Reading room/Proposals/2022/December

Special section to allow some degrees of fiction and literature
I propose allowing collective fictional story/literature books under a special section that do not pursuade a strong bias of opinion. Comrade-yutyo (discuss • contribs) 14:15, 20 July 2022 (UTC)

 * How would that meet our goal of being an educational resource? —Justin ( koavf ) ❤T☮C☺M☯ 22:42, 8 November 2022 (UTC)
 * I agree. I can't see how it would help. I think there's already a wiki for user-made fiction, but it aint a wikimedia wiki. And I forgot the name of it too. 20:48, 16 November 2022 (UTC)
 * A collaborative basal reader alongside the existing non-fiction Wikijunior project might be feasible if editors in the fiction section agree to write in the sort of simplified English seen on Simple English Wikipedia. Otherwise, we could leave the job to StoryWeaver by Pratham Books. Damian Yerrick (discuss • contribs) 18:54, 30 November 2022 (UTC)
 * My 2 cents : According to Wikibooks, your proposal is not in scope of Wikibooks projects
 * As a general rule, only instructional books are suitable for inclusion. Non-fictional books (as well as fictional ones) that aren't instructional aren't allowed on Wikibooks. Commented by Encik Tekateki (discuss • contribs) 07:04, 1 December 2022 (UTC)

Adding books AI Art Generation Handbook to Wikibooks Stacks/Shelf:Visual arts
Hi, just thought out to propose this idea and see if anyone is have the authority to move my newly created books AI Art Generation Handbook into Shelf:Visual arts ? I notice the category is uncategroized after noticing the books footer recently. MrAlanKoh (discuss • contribs) 06:20, 30 November 2022 (UTC)
 * Or my 2nd thought, how about Shelf:Computer art ? Thank you.
 * , could you help to move it to appropriate shelf Shelf:Visual arts or Shelf:Computer art ? Encik Tekateki (discuss • contribs) 10:40, 27 December 2022 (UTC)
 * I've done it. In the future, if you want to do this again, just add  to the bottom of the page.  11:16, 27 December 2022 (UTC)


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Wikibooks:Reading room/Proposals/2022/November

Cookbook style guide
Hello! I am proposing some modifications to the Cookbook recipe style guide/recipe template. Feedback is welcome at that talk page. Thanks! —Kittycataclysm (discuss • contribs) 13:24, 30 November 2022 (UTC)


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Wikibooks:Requests for permissions/Kittycataclysm

(temporary adminship)
Hello! Per @Leaderboard's suggestion, I am requesting temporary adminship to help combat LTA (especially GRP) on wikibooks. The duration of the temporary adminship is hard to specify, but generally it would be for while we are experiencing high volumes of LTA. My main goals are to be able to protect/semi-protect pages where LTA is happening and to block IPs that appear to be engaging in LTA. Thanks! —Kittycataclysm (discuss • contribs) 16:06, 21 December 2022 (UTC)
 * Another goal of mine that would require temp adminship is to delete pages for which it is unambiguously necessary, such as pages that consist solely of vandalism or blank pages. —Kittycataclysm (discuss • contribs) 16:33, 21 December 2022 (UTC)
 * Support. I trust the user. 18:43, 21 December 2022 (UTC)
 * I also support the request; we're rather short on active admins here. We can start off with a 3 month temporary adminship for the purposes of anti-vandalism and see how that goes - if you find the rights helpful, we can make it indefinite. Leaderboard (discuss • contribs) 19:44, 21 December 2022 (UTC)
 * I support this too - as someone actively trying to remove his vandalism from multiple WMF projects, we could really use extra admin help. Thank you - Antandrus (discuss • contribs) 23:38, 21 December 2022 (UTC)

 * this project is definitely needs more admins. I've gone hours waiting for an admin, only for various LTAs to be disinterested in that specific page (I'm not just referring to GRP here). I'd maybe expect a little more x-wiki experience, but I'm still happy granting temp-admin privileges. --SHB2000 (discuss • contribs) 10:53, 22 December 2022 (UTC)
 * @MarcGarver Would you be the person to reach out to for next steps on this? I think you're the only bureaucrat on Wikibooks. Thanks! —Kittycataclysm (discuss • contribs) 18:11, 26 December 2022 (UTC)
 * MarcGarver has unfortunately not been active since Dec 15 on any WMF project (per the GUC tool). Should a request be made on SRP (as an exception to the norm)? SHB2000 (discuss • contribs) 02:51, 27 December 2022 (UTC)
 * No need to hurry anything. Be patient.--Xania [[Image:Flag_of_Estonia.svg|15px]] [[Image:Flag_of_Ukraine.svg|15px]] talk 03:05, 27 December 2022 (UTC)
 * Oops, I apologize @Xania! I had assumed this was urgent based on other users' commentary, and I wanted to clarify the path forward, but I am more than happy to wait if other people are taking care of the existing issues :) —Kittycataclysm (discuss • contribs) 15:28, 27 December 2022 (UTC)
 * We don't have temporary admins, and we have no process other than a "removal" RFP to remove the "temporary" admin. You need a policy change FIRST because otherwise a Steward will not remove the admin flag and only Stewards can remove it, not WB bureaucrats. I suggest you propose the policy change for approval first. The support so far is insufficient for a permanent admin which is all we can currently do. MarcGarver (discuss • contribs) 09:37, 3 January 2023 (UTC)
 * Okay clearly having a brain fade. Yes we do have temporary admins now - I recall the policy changing some time ago. However, I would like to see more than four supports before actioning. MarcGarver (discuss • contribs) 12:18, 3 January 2023 (UTC)

 * need more folks taking care of the recent LTA. —Atcovi (Talk - Contribs) 01:21, 5 January 2023 (UTC)

(extension of temp adminship)
Hello! I believe my temporary adminship is set to expire in about 1 month on April 7 2023. I would like to proactively request extension of the temporary adminship so that I can continue contributing the the community and preventing vandalism. I have been regularly preventing vandalism for the last couple months, and I would like to continue being able to do so. Cheers! —Kittycataclysm (discuss • contribs) 19:57, 10 March 2023 (UTC)
 * permanent adminship, though I realise that this is up to the discretion of the stewards. You've been the most active admin as of the past three months – your inclusion to the admin team will definitely be more than just "another admin". --SHB2000 (discuss • contribs) 22:14, 10 March 2023 (UTC)
 * Svartava (discuss • contribs) 03:45, 14 March 2023 (UTC)
 * per SHB2000 [//xtools.wmflabs.org/adminstats/en.wikibooks.org/2023-01-21?actions=delete|revision-delete|log-delete|restore|re-block|unblock|re-protect|unprotect|rights|merge|import|abusefilter|contentmodel crosschecked]. JackPotte (discuss • contribs) 07:36, 14 March 2023 (UTC)
 * for permanent. Leaderboard (discuss • contribs) 18:02, 26 March 2023 (UTC)


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User talk:100.40.31.99

Blocked
You cannot edit for 1 week due to disruptive editing. You are welcome to contribute constructively when editing privileges are restored. You may appeal this action with a reasonable argument by adding the text below.

--Xania   talk 01:31, 31 July 2023 (UTC)

You cannot edit for 2 weeks due to disruptive editing. You are welcome to contribute constructively when editing privileges are restored. You may appeal this action with a reasonable argument by adding the text below.

--Xania   talk 01:30, 7 September 2023 (UTC)

title: User talk:100.40.31.99


text: User talk:131.109.124.108

title: User talk:131.109.124.108


text:
Guide to the Godot game engine/Optimisation

Games run on machines that have a finite amount of resources and CPU power. There is only so much a phone or computer can do before your game starts to lag. There are good ways to fix this though.

Less is more
Try to reduce how much code runs. The best way to increase your game's FPS is to stop code from running that does not need to run. For e.g, you don't need to calculate collision of an object twice per frame, or if you never get to collide with that object in the first place. For often updated objects, try delaying the update for the next frame, and using the most recent update attempt.

Minimise math
When the game makes complex calculation every frame, the game gets quite laggy. Try storing the calculations in a variable before using it. Calls to  also cause a little lag, so try to cache those in a variable as well.

Avoid this:

direction (delta): turret = direction = .UP.(turret.rotation)

Prefer this:

direction turret = (delta): direction = .UP.(turret.rotation)

The after code sample is 1% faster than the before. It may not sound like much, but doing this optimisation to all your code adds up. Larger functions with many calls to  or   makes more lag than caching it to a variable beforehand.

There is no need to do more math than necessary. Cache the results of calculations that stay the same before loops.

This is slow:

alloy_strength = alloy_thickness = alloy_layers = robot army: robot.armour = (alloy_strength * alloy_thickness, alloy_layers) + robot.native_armour

This is faster:

alloy_strength = alloy_thickness = alloy_layers = base_armour = (alloy_strength * alloy_thickness, alloy_layers) robot army: robot.armour = base_armour + robot.native_armour

Iterate over a 1D array over an of Arrays
Accessing a small Array only once or twice is negligible. But for larger Arrays that are accessed often, the performance difference adds up.

x : y : element = my_array[y][x] x : y : element = my_array[y * + x]  i  : element = my_array[i]

By using Godot's native iterator instead of making your own, you get a larger performance boost:

x : element my_array[x]: element my_array:

You may not always be able to do this, but it is worth keeping in mind. The main things to takeaway from this when working with arrays are:
 * 1D Arrays are faster than multi-dimensional Arrays
 * Single loops are faster than nested loops
 * Accessing an array element with the iterator for element in array is faster than using indices, e.g..

Remove items from the back of an Array
Whenever you remove or add an item from an array, it needs to re-index every item after it, which means adding/removing an item from the beginning of an array re-indexes more items than when adding/removing an item from the end of an array.

Favor  and   or   when removing or adding elements to an array. Avoid  and.

Use the right data type for the job
These are three rules of thumb to help you decide if you should use an Array or a Dictionary.
 * If you want to remove elements from a collection at any location and at any time, use a.
 * If you will access elements from a collection by key randomly, use a Dictionary.
 * If you lay your elements in order, use an.

Don't use
The  function is slow. Try to not use it when you export the game. Instead make your own logging function and autoload it. An example:

log =. :  log.(, .WRITE) :  log. (message, source): current_time =. log.( % [source, current_time.hour, current_time.minute, current_time.second, message])

Use statically typed GDScript in Godot 4.x
The gains of using static typing are very high, between 50% to 150% gains. The reasoning of this, in short, is that Godot is putting less effort into finding out what type everything is to run the correct logic.

Use, not
If trying to get a node's grandparent (or an upper node), don't use, since it is 37% slower than just using. It looks more messy, sure, but if you need faster code, this is how you will want to do it.

Use the latest stable version
Newer stable Godot versions have more optimizations and tend to be more performant than older ones.


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Native American Management Practices

INTRODUCTION

American Indians have a long and proud history of productive trade, commerce, and entrepreneurship. Existing long before European colonialism, Native people created and sustained numerous and complex trade and barter alliances that provided for their various needs.

Native American management practices differ from conventional American management practices in several ways. This book will elaborate on those differences. The aim will be to foster a culturally relevant perspective that leads to understanding and appreciation of those differences. This is a largely missing element in the management domain. As Native American Tribes expand their enterprises and create employment opportunities, it will become increasingly important to understand Native American management philosophy, principles and practices and how they differ from at-large American management practices.

The United States Government Accounting Office states "There are 574 ethnically, culturally, and linguistically diverse federally recognized Indian Tribes in the United States. These Tribes are distinct political entities whose inherent sovereignty predates the United States and is reflected in their government-to-government relationship with the U.S. government. The United States has undertaken a unique trust responsibility to protect and support Tribes and their members through treaties, statutes, and historical relations with Tribes.

Attempts to elaborate and contrast Native American management and leadership practices have been undertaken by Bryant (1996), Kennedy & Harrington (2017) and Ravilochan (2021), to name a few.

HISTORY

In 1975, the U.S. government passed the Indian Self-Determination and Education Assistance Act. As a result, tribes have developed organizations to administer their own social, welfare and housing programs. They have also established several tribal colleges as well as launched and acquired numerous businesses around the United States. Consequently, there exists a practical need to identify and differentiate Native American management practices from conventional management practices.

There are 500 plus tribes scattered throughout the United States, with tribes, nations, communities, and bands holding about 2 percent of U.S. land. Not many citizens know the extent of tribal lands partially because these communities are virtually invisible politically and economically. Well before the arrival of the Spanish explorers, Native American people hunted, farmed, and traded over all of what is now the United States, as well as the rest of North and South America. European countries competed for political and military alliances with North American tribes through nation-to-nation treaties that are the basis of U.S. Indian law today.

Many United States Policies targeted Native American Language and Culture for destruction in an effort to assimilate the Native American people and their identities. When these policies were taking place Native American leadership was disrupted, dismantled, and destroyed because these policies consumed the lives of the people who remained as second-class citizens in lands their ancestors ruled for millennia. Other policies such as the general Allotment Act worked to break up land ownership and assimilate Native American people while the Indian Reorganization Act manipulated tribal leadership, creating a one-size-fits-all Western model of government. Many adapted but kept aspects of the culture ingrained in their societies despite centuries of assimilation and educational policies designed to eradicate Native American culture. U.S. policies in the past targeted leadership, so we now need a specific approach to regain our ways of leadership training. The Native American Leadership Model is a source for understanding leadership styles through a lens of tribal core values and Indigenous learning methodologies.

TRADITION

There are many native American tribes in North America. One of these tribes called the Anishinaabe, which embraces an ethical system called The Seven Grandfather Teachings, to allow for the best decisions for mother earth and the community: These seven teachings include: Wisdom, Love, Respect, Bravery, Honesty, Humility, and Truth. These teachings work together to help the community and individual make responsible decisions.

Many people believe Native Americans did not have trade and commerce before European contact. However, according to author Charles F. Harrington in his book American Indian Business Principles and Practices, he states. " Existing long before European colonialism, Native people created and sustained numerous and complex trade and barter alliances that provided for their needs." Trade has always apart of Native American culture, Harrington also says in his book " The welfare and survival of early Native Americans depended entirely on their own resourcefulness, ingenuity, and persistence. They created and sustained their own cultures and societies, technologies, and means of production, habitats, and both land and water-based transportation routes." Native Americans were not just hunter gatherers they practiced trade and had close business alliances with other tribal nation long before European contact.

LAW

The Tribal Law and Order Act (July 29, 2010) helps to address crime in tribal communities and places a strong emphasis on decreasing violence against American Indian and Alaska Native women. With respect to business, tribally owned businesses must be structured according to state or federal laws it is up to the tribes to select, they can become tribal chartered corporation or a state chartered tribal corporation.

Native American communities that are recognized by the United States federal government are considered Sovern nations. As sovereign entities, Native American communities are guaranteed the power and/or right to determine their form of government, define citizenship, make and enforce laws through their own police force and courts, collect taxes, and regulate property use. There are 574 federally-recognized American Indian and Alaska Native nations in the U.S., according to the federal Bureau of Indian Affairs. Each is a government entity with its own policies, processes and system of governance. Although, the US government still maintains the responsibility of protecting Native American assets and resources while also serving as a trustee for Native American lands.

The Native American Law and Sovereignty Institute is an institution that helps law students who are Native American or anyone who has interest in Native American Law. They have four main components with the first one being an academic component offering a certificate for students to specialize in this area. This certificate allows students to explore the cross-cultural fields of Native American law. The second one being a public education component through events, such as their annual conference. The third one being advising for the Native American Law Student Association or NALSA. The fourth one being a commitment to their Law students by focusing on recruitment, retention, graduation, and alumni connections. This institute is important because we need to have well educated legal practitioners to assist in maintaining our homelands, ensuring treaty provisions are upheld, and in furthering tribal sovereignty. This institute prioritizes supporting the cultural and traditional Indigenous legal principles. Some topics that are covered in this institute are the Indian Child Welfare Act (ICWA), tribal gaming, treaty rights, the relationship between tribal nations and other governmental entities and systems and many more.

LEADERSHIP

There are six concepts that can be found when looking at Native American leadership styles or practices. These six concepts are decentralized leadership, the immanent value of all things, a value of non-interference, a self deflecting image projection, a reduced sense of the importance of time, and a collectivist decision making approach. Decentralized leadership means to give power to individuals who are closer to the information to make valuable decisions. The immanent value of all things means all individuals have unique perspectives to contribute to the business. A value of non-interference means to promote positive relationships by discouraging coercion of any kind. A self deflecting image projection means to take the blame off of oneself. A reduced sense of the importance of time means to do projects with ease and to take your time to produce the best outcomes. A collectivist decision making approach means to place emphasis on the group or community, not on an individual. These are very important to remember when thinking about Native American Management Practices.

MANAGEMENT

Conventional management practices focus more on the individual whereas Native American management practices focus more on the community and how decisions effect everyone. Native American management practices involve the processes of Self Actualization, Community Actualization, and Culture Perpetuity. Self Actualization from a Native American point of view means that they are born self-actualized and are given space to express who they are through trusting them. In order to live up to this Native American peoples are presented with education, prayer, ceremonies, individual experience, etc. This helps them come into the sacred world to become more self-actualized. Community Actualization from a Native American point of view means that they all work together to solve issues such as, meeting basic needs and ensuring safety. By having community actualization they create a community of trust, generosity, and cooperation. This helps them to make decisions more clear and beneficial to the community. Culture Perpetuity from a Native American point of view means to transfer wisdom from generation to generation to support Self Actualization and Community Actualization. This is done by formal rituals and informal apprenticeships teachings that are communicated from an elder to a child. Each generation can add something new that they have learned to keep the traditions alive.

ENTREPRENEURSHIP

Many tribes have begun investing both on and off their tribal lands. These investments can be seen in hotels, golf courses, manufacturing, entertainment venues, solar and wind technology, tourism and the hospitality industry, health care, and gambling enterprises all of which create revenue for the community. Over the last few decades tribes have been making changes to improve their economies while providing their communities with a better quality of life.

The Federal Reserve Bank of Minnesota noted that over the last 25 years, the number of tribal enterprises outside of gaming has grown significantly. It goes on to report that "The expansion of 8(a) Program eligibility has supported the economic development of tribal nations through the creation of new tribal enterprises." Gaming is one of the biggest enterprises that has helped all tribes across the country. Tribal governments use the gaming revenues to fund social service projects like scholarships, health-care clinics, substance abuse programs, education, law enforcement and tribal court. They also use to revenues for infrastructure development such as new roads, new sewer and drainage system, housing, and water systems. Gaming isn't the only tribal enterprise helping different tribes. A lot of tribes are now getting into the Cannabis Industry. The first tribe in Michigan to get cannabis legalized federally on tribal reservations was Bay Mills Indian Community.

TRAINING & DEVELOPMENT

The National Indian and Native American Employment and Training Conference (www.NINAETC.com), which is described as "a representative voice of consensus on national employment and training issues," is an annual conference that offers an opportunity for various tribal governments and organizations to come together to provide and exchange information, recognize those who benefit from training/employment programs, and have an open space to develop or create new ideas.

ANISHINAABE
The World Atlas states "Anishinaabe means ‘Original people’. It is a collective name for groups of indigenous people who live in the US and Canada. Anishinaabe people are comprised of several Algonquian tribes including Potawatomi, Algonquin, Ojibway, Mississauga, Nipissing, Saulteaux, Ottawa, and Oji-Cree communities. These communities have a common origin and they share cultural values and traditions. Anishinaabe people were commonly found in the Subarctic region and the Great Lakes region." The encyclopaedia Britannica goes on to explain further that the "Ojibwa, also spelled Ojibwe or Ojibway, also called Chippewa, self-name Anishinaabe, Algonquian-speaking North American Indian tribe who lived in what are now Ontario and Manitoba, Can., and Minnesota and North Dakota, U.S., from Lake Huron westward onto the Plains. Their name for themselves means “original people.” In Canada those Ojibwa who lived west of Lake Winnipeg are called the Saulteaux."

Sustainability and Seventh Generation.
The Seventh-Generation principle originated from the Haudenosaunee people. It is included in The Great Law of Haudenosaunee Confederacy. Businesses who incorporate the Seventh-Generation principle have a more social and environmental awareness to the effects that their business operations cause. According to Indigenous Corporate Training Inc., this philosophy guides “the decisions we make today should result in a sustainable world seven generations into the future. The Seventh-Generation Principle describes a mindset we must adopt while making our daily life choices. We are to be mindful of the seven generations that came before us and the seven generations that come after us. The world we inhabit is not strictly ours because we are here now. It is given as a gift from our ancestors and we are merely borrowing it from those to come. That means we hold it equally with all of those we are related too. We need to be aware of the negative environmental impact of our actions and try to leave Mother Earth better for our future relations.

COOPERATION
Cooperation between Anishinaabe communities is still prolific in the 21st century. Recently Odawa Economic Affairs Holding Corporation, Mno-Bmadsen, and Gun Lake Investments, which are economic development arms of the Little Traverse Bay Band of Odawa Indians, the Pokagon Band of Potawatomi Indians, and the Gun Lake Tribe respectfully, have announced a historic joint venture called Aki Construction whose aim, according to Shanna Shananaquet, Executive Director of Odawa Holdings, in a press release by the Little Traverse Bay Band of Odawa Indians, "is to be a leader with Tribal-driven construction projects within the Midwest and across the country." This joint venture is touted as the first of its kind in the Midwest.

Personal Contributions/Thoughts
One student observed "After researching about the Native American Management Practices I found major differences compared to conventional textbook concepts. Since I grew up on the reservation while attending a predominantly white school I had already predicted there were going to be major differences because I had experienced them first-hand. One major difference I noticed between Native American Management and Management is that Native American Management focuses more on the spiritual fulfillment and enlightenment, whereas conventional/textbook Management exploits resources for commercial profit. There are no social classes in Native American Management compared to Management. I noticed this difference when attending school as I felt much more relaxed and eased in with my Native American educators compared to others that did not view the Native American side of Management as correct."

Works Cited “7 Grandfather Teachings .” Saginaw Chippewa Indian Tribe of Michigan Homepage,

title: Native American Management Practices


text:
Talk:Native American Management Practices

An UN-elaborated Topic
This is an effort to contribute a missing aspect of management principles. Dpfowler (discuss • contribs) 17:08, 1 April 2023 (UTC)

We are writing a Wikibook on Native American Management Practices in order to differentiate them from extant practices.

Original contributions to this effort will be made by business majors at the Saginaw Chippewa Tribal College (SCTC). Their efforts will eventually be shared with business majors at other tribal colleges, inviting them to make their own contributes to this development.

Founding Members of this initiative include:

Joseph Fisher, Jayde Byrne, Dale Fowler, Gordan Bennett, Jenessa Johnson, Katheryn Pierce, Macy Rulapaugh, Aicia Raphael, Naomi McCormack, and Sierra Snyder-VanArsdale.


text: Template:Template other

title: Template:Template other


text: Template:Pp

title: Template:Pp


text:
Template:TemplateData header

TemplateData for

title: Template:TemplateData header


text: Template:Template parameter usage

title: Template:Template parameter usage


text:
Module:Check for unknown parameters

-- This module may be used to compare the arguments passed to the parent -- with a list of arguments, returning a specified result if an argument is -- not on the list local p = {}

local function trim(s) return s:match('^%s*(.-)%s*$') end

local function isnotempty(s) return s and s:match('%S') end

local function clean(text) -- Return text cleaned for display and truncated if too long. -- Strip markers are replaced with dummy text representing the original wikitext. local pos, truncated local function truncate(text) if truncated then return '' end if mw.ustring.len(text) > 25 then truncated = true text = mw.ustring.sub(text, 1, 25) .. '...'		end return mw.text.nowiki(text) end local parts = {} for before, tag, remainder in text:gmatch('([^\127]*)\127[^\127]*%-(%l+)%-[^\127]*\127') do		pos = remainder table.insert(parts, truncate(before) .. '<' .. tag .. '>...</' .. tag .. '>') end table.insert(parts, truncate(text:sub(pos or 1))) return table.concat(parts) end

function p._check(args, pargs) if type(args) ~= "table" or type(pargs) ~= "table" then -- TODO: error handling return end

-- create the list of known args, regular expressions, and the return string local knownargs = {} local regexps = {} for k, v in pairs(args) do		if type(k) == 'number' then v = trim(v) knownargs[v] = 1 elseif k:find('^regexp[1-9][0-9]*$') then table.insert(regexps, '^' .. v .. '$') end end

-- loop over the parent args, and make sure they are on the list local ignoreblank = isnotempty(args['ignoreblank']) local showblankpos = isnotempty(args['showblankpositional']) local values = {} for k, v in pairs(pargs) do		if type(k) == 'string' and knownargs[k] == nil then local knownflag = false for _, regexp in ipairs(regexps) do				if mw.ustring.match(k, regexp) then knownflag = true break end end if not knownflag and ( not ignoreblank or isnotempty(v) ) then table.insert(values, clean(k)) end elseif type(k) == 'number' and knownargs[tostring(k)] == nil then local knownflag = false for _, regexp in ipairs(regexps) do				if mw.ustring.match(tostring(k), regexp) then knownflag = true break end end if not knownflag and ( showblankpos or isnotempty(v) ) then table.insert(values, k .. ' = ' .. clean(v)) end end end

-- add results to the output tables local res = {} if #values > 0 then local unknown_text = args['unknown'] or 'Found _VALUE_, '

if mw.getCurrentFrame:preprocess( "" ) == "" then local preview_text = args['preview'] if isnotempty(preview_text) then preview_text = require('Module:If preview')._warning({preview_text}) elseif preview == nil then preview_text = unknown_text end unknown_text = preview_text end for _, v in pairs(values) do			-- Fix odd bug for | = which gets stripped to the empty string and -- breaks category links if v == '' then v = ' ' end

-- avoid error with v = 'example%2' ("invalid capture index") local r = unknown_text:gsub('_VALUE_', {_VALUE_ = v}) table.insert(res, r)		end end

return table.concat(res) end

function p.check(frame) local args = frame.args local pargs = frame:getParent.args return p._check(args, pargs) end

return p

title: Module:Check for unknown parameters


text: Template:Ombox

title: Template:Ombox


text:
Module:High-use

local p = {}

-- _fetch looks at the "demo" argument. local _fetch = require('Module:Transclusion_count').fetch local yesno = require('Module:Yesno')

function p.num(frame, count) if count == nil then if yesno(frame.args['fetch']) == false then if (frame.args[1] or ) ~=  then count = tonumber(frame.args[1]) end else count = _fetch(frame) end end -- Build output string local return_value = "" if count == nil then if frame.args[1] == "risk" then return_value = "a very large number of" else return_value = "many" end else -- Use 2 significant figures for smaller numbers and 3 for larger ones local sigfig = 2 if count >= 100000 then sigfig = 3 end -- Prepare to round to appropriate number of sigfigs local f = math.floor(math.log10(count)) - sigfig + 1 -- Round and insert "approximately" or "+" when appropriate if (frame.args[2] == "yes") or (mw.ustring.sub(frame.args[1],-1) == "+") then -- Round down return_value = string.format("%s+", mw.getContentLanguage:formatNum(math.floor( (count / 10^(f)) ) * (10^(f))) ) else -- Round to nearest return_value = string.format("approximately %s", mw.getContentLanguage:formatNum(math.floor( (count / 10^(f)) + 0.5) * (10^(f))) ) end

-- Insert percentage of pages if that is likely to be >= 1% and when |no-percent= not set to yes if count and count > 250000 and not yesno (frame:getParent.args['no-percent']) then local percent = math.floor( ( (count/frame:callParserFunction('NUMBEROFPAGES', 'R') ) * 100) + 0.5) if percent >= 1 then return_value = string.format("%s pages, or roughly %s%% of all", return_value, percent) end end end return return_value end

-- Actions if there is a large (greater than or equal to 100,000) transclusion count function p.risk(frame) local return_value = "" if frame.args[1] == "risk" then return_value = "risk" else local count = _fetch(frame) if count and count >= 100000 then return_value = "risk" end end return return_value end

function p.text(frame, count) -- Only show the information about how this template gets updated if someone -- is actually editing the page and maybe trying to update the count. local bot_text = (frame:preprocess("") == "") and "\n\n\nPreview message: Transclusion count updated automatically (see documentation)." or '' if count == nil then if yesno(frame.args['fetch']) == false then if (frame.args[1] or ) ~=  then count = tonumber(frame.args[1]) end else count = _fetch(frame) end end local title = mw.title.getCurrentTitle if title.subpageText == "doc" or title.subpageText == "sandbox" then title = title.basePageTitle end local systemMessages = frame.args['system'] if frame.args['system'] == '' then systemMessages = nil end -- This retrieves the project URL automatically to simplify localiation. local templateCount = ('on %s pages'):format(		mw.title.getCurrentTitle:fullUrl:gsub('//(.-)/.*', '%1'),		mw.uri.encode(title.fullText), p.num(frame, count)) local used_on_text = "'''This " .. (mw.title.getCurrentTitle.namespace == 828 and "Lua module" or "template") .. ' is used '; if systemMessages then used_on_text = used_on_text .. systemMessages .. ((count and count > 2000) and ("''', and " .. templateCount) or ("'''")) else used_on_text = used_on_text .. templateCount .. "'''"	end local sandbox_text = ("%s's /sandbox or /testcases subpages, or in your own %s. "):format(		(mw.title.getCurrentTitle.namespace == 828 and "module" or "template"),		title.fullText, title.fullText,		mw.title.getCurrentTitle.namespace == 828 and "Module:Sandbox|module sandbox" or "w:Wikipedia:User pages#SUB|user subpage"	) local infoArg = frame.args["info"] ~= "" and frame.args["info"] if (systemMessages or frame.args[1] == "risk" or (count and count >= 100000) ) then local info = systemMessages and '. Changes to it can cause immediate changes to the Wikibooks user interface.' or '.' if infoArg then info = info .. " " .. infoArg end sandbox_text = info .. ' To avoid major disruption' .. (count and count >= 100000 and ' and server load' or '') .. ', any changes should be tested in the ' .. sandbox_text .. 'The tested changes can be added to this page in a single edit. '	else sandbox_text = (infoArg and ('. ' .. infoArg .. ' C') or ' and c') .. 'hanges may be widely noticed. Test changes in the ' .. sandbox_text end

local discussion_text = systemMessages and 'Please discuss changes ' or 'Consider discussing changes ' if frame.args["2"] and frame.args["2"] ~= "" and frame.args["2"] ~= "yes" then discussion_text = string.format("%sat %s", discussion_text, frame.args["2"]) else discussion_text = string.format("%son the talk page", discussion_text, title.talkPageTitle.fullText ) end return used_on_text .. sandbox_text .. discussion_text .. " before implementing them." .. bot_text end

function p.main(frame) local count = nil if yesno(frame.args['fetch']) == false then if (frame.args[1] or ) ~=  then count = tonumber(frame.args[1]) end else count = _fetch(frame) end local image = "" local type_param = "style" local epilogue = '' if frame.args['system'] and frame.args['system'] ~= '' then image = "" type_param = "content" local nocat = frame:getParent.args['nocat'] or frame.args['nocat'] local categorise = (nocat == '' or not yesno(nocat)) if categorise then epilogue = frame:preprocess('') end elseif (frame.args[1] == "risk" or (count and count >= 100000)) then image = "" type_param = "content" end if frame.args["form"] == "editnotice" then return frame:expandTemplate{ title = 'editnotice', args = { ["image"] = image, ["text"] = p.text(frame, count), ["expiry"] = (frame.args["expiry"] or "") }		} .. epilogue else return require('Module:Message box').main('ombox', {			type = type_param,			image = image,			text = p.text(frame, count),			expiry = (frame.args["expiry"] or "")		}) .. epilogue end end

return p

title: Module:High-use


text:
Module:Transclusion count

local p = {}

function p.fetch(frame) local template = nil local return_value = nil

-- Use demo parameter if it exists, otherswise use current template name local namespace = mw.title.getCurrentTitle.namespace if frame.args["demo"] and frame.args["demo"] ~= "" then template = mw.ustring.gsub(frame.args["demo"],"^[Tt]emplate:","") elseif namespace == 10 then -- Template namespace template = mw.title.getCurrentTitle.text elseif namespace == 828 then -- Module namespace template = (mw.site.namespaces[828].name .. ":" .. mw.title.getCurrentTitle.text) end

-- If in template or module namespace, look up count in /data if template ~= nil then namespace = mw.title.new(template, "Template").namespace if namespace == 10 or namespace == 828 then template = mw.ustring.gsub(template, "/doc$", "") -- strip /doc from end template = mw.ustring.gsub(template, "/sandbox$", "") -- strip /sandbox from end local index = mw.ustring.sub(mw.title.new(template).text,1,1) local status, data = pcall(function 				return(mw.loadData('Module:Transclusion_count/data/' .. (mw.ustring.find(index, "%a") and index or "other"))) 			end) if status then return_value = tonumber(data[mw.ustring.gsub(template, " ", "_")]) end end end -- If database value doesn't exist, use value passed to template if return_value == nil and frame.args[1] ~= nil then local arg1=mw.ustring.match(frame.args[1], '[%d,]+') if arg1 and arg1 ~= '' then return_value = tonumber(frame:callParserFunction('formatnum', arg1, 'R')) end end return return_value end

-- Tabulate this data for Database reports/Templates transcluded on the most pages function p.tabulate(frame) local list = {} for i = 65, 91 do local data = mw.loadData('Module:Transclusion count/data/' .. ((i == 91) and 'other' or string.char(i))) for name, count in pairs(data) do			table.insert(list, {mw.title.new(name, "Template").fullText, count}) end end table.sort(list, function(a, b)		return (a[2] == b[2]) and (a[1] < b[1]) or (a[2] > b[2])	end) local lang = mw.getContentLanguage; for i = 1, #list do		list[i] = ('|-\n| %d || %s || %s\n'):format(i, list[i][1]:gsub('_', ' '), lang:formatNum(list[i][2])) end return table.concat(list) end

return p

title: Module:Transclusion count


text: Template:Lua

title: Template:Lua


text: Template:Module other

title: Template:Module other


text:
Module:Transclusion count/data/C

return { ["C"] = 599000, ["C-Class"] = 77000, ["C-SPAN"] = 12000, ["C-cmn"] = 2600, ["C-pl"] = 52000, ["C."] = 3500, ["CAN"] = 20000, ["CANelec"] = 13000, ["CANelec/gain"] = 2500, ["CANelec/hold"] = 4600, ["CANelec/source"] = 6800, ["CANelec/top"] = 6000, ["CANelec/total"] = 5900, ["CAS"] = 3800, ["CBB_Standings_End"] = 15000, ["CBB_Standings_Entry"] = 15000, ["CBB_Standings_Start"] = 15000, ["CBB_Yearly_Record_End"] = 2900, ["CBB_Yearly_Record_Entry"] = 3000, ["CBB_Yearly_Record_Start"] = 2900, ["CBB_Yearly_Record_Subhead"] = 3500, ["CBB_Yearly_Record_Subtotal"] = 2800, ["CBB_roster/Footer"] = 7600, ["CBB_roster/Header"] = 7600, ["CBB_roster/Player"] = 7600, ["CBB_schedule_end"] = 10000, ["CBB_schedule_entry"] = 10000, ["CBB_schedule_start"] = 10000, ["CBB_yearly_record_end"] = 4000, ["CBB_yearly_record_end/legend"] = 3500, ["CBB_yearly_record_entry"] = 3900, ["CBB_yearly_record_start"] = 3900, ["CBB_yearly_record_subhead"] = 3600, ["CBB_yearly_record_subtotal"] = 3700, ["CBSB_Standings_End"] = 4300, ["CBSB_Standings_Entry"] = 4300, ["CBSB_Standings_Start"] = 4300, ["CBSB_link"] = 3400, ["CC0"] = 4300, ["CENTURY"] = 16000, ["CFB_Standings_End"] = 33000, ["CFB_Standings_Entry"] = 33000, ["CFB_Standings_Start"] = 33000, ["CFB_Yearly_Record_End"] = 6500, ["CFB_Yearly_Record_End/legend"] = 2300, ["CFB_Yearly_Record_Entry"] = 6500, ["CFB_Yearly_Record_Start"] = 6500, ["CFB_Yearly_Record_Subhead"] = 6600, ["CFB_Yearly_Record_Subtotal"] = 6400, ["CFB_schedule"] = 25000, ["CFB_schedule_entry"] = 18000, ["CFL_Year"] = 5500, ["CGF_year"] = 2600, ["CHE"] = 10000, ["CHI"] = 2800, ["CHL"] = 3600, ["CHN"] = 11000, ["CN"] = 2800, ["CO2"] = 3000, ["COI"] = 14000, ["COIUL"] = 127000, ["COI_editnotice"] = 6600, ["COL"] = 4900, ["COLON"] = 13000, ["CRI"] = 2200, ["CRO"] = 4200, ["CSK"] = 2800, ["CSS_image_crop"] = 4300, ["CUB"] = 3600, ["CURRENTDATE"] = 3600, ["CURRENTISOYEAR"] = 2100, ["CURRENTMINUTE"] = 2500, ["CYP"] = 2000, ["CZE"] = 15000, ["Calendar"] = 2400, ["California/color"] = 11000, ["Call_sign_disambiguation"] = 3000, ["Campaignbox"] = 22000, ["CanProvName"] = 13000, ["CanadaByProvinceCatNav"] = 9800, ["CanadaProvinceThe"] = 4000, ["Canadian_English"] = 6300, ["Canadian_Parliament_links"] = 5100, ["Canadian_election_result"] = 13000, ["Canadian_election_result/gain"] = 2600, ["Canadian_election_result/hold"] = 4700, ["Canadian_election_result/source"] = 7800, ["Canadian_election_result/top"] = 13000, ["Canadian_election_result/top/ElectionYearTest"] = 5600, ["Canadian_election_result/total"] = 10000, ["Canadian_party_colour"] = 7800, ["Canadian_party_colour/colour"] = 17000, ["Canadian_party_colour/colour/default"] = 17000, ["Canadian_party_colour/name"] = 14000, ["Canadian_party_colour/name/default"] = 6400, ["Canned_search"] = 4900, ["Cascite"] = 15000, ["Caselaw_source"] = 4000, ["Cassini-Ehess"] = 2600, ["Cast_listing"] = 14000, ["Castlist"] = 2100, ["Cat"] = 335000, ["CatAutoTOC"] = 644000, ["CatAutoTOC/core"] = 53000, ["CatRel"] = 3800, ["CatTrack"] = 3100, ["Cat_class"] = 6600, ["Cat_in_use"] = 49000, ["Cat_main"] = 182000, ["Cat_more"] = 100000, ["Cat_more_if_exists"] = 45000, ["Cat_see_also"] = 3500, ["Catalog_lookup_link"] = 510000, ["Category"] = 17000, ["Category-Class"] = 14000, ["Category-inline"] = 8700, ["Category_TOC"] = 71000, ["Category_TOC/tracking"] = 71000, ["Category_U.S._State_elections_by_year"] = 7300, ["Category_U.S._State_elections_by_year/core"] = 7300, ["Category_class"] = 35000, ["Category_class/column"] = 35000, ["Category_class/second_row_column"] = 35000, ["Category_described_in_year"] = 5700, ["Category_diffuse"] = 7900, ["Category_disambiguation"] = 2300, ["Category_disambiguation/category_link"] = 2300, ["Category_explanation"] = 78000, ["Category_handler"] = 3260000, ["Category_ifexist"] = 5000, ["Category_importance"] = 10000, ["Category_importance/column"] = 10000, ["Category_importance/second_row_column"] = 10000, ["Category_link"] = 133000, ["Category_link_with_count"] = 6600, ["Category_more"] = 110000, ["Category_more_if_exists"] = 45000, ["Category_ordered_by_date"] = 11000, ["Category_other"] = 873000, ["Category_redirect"] = 104000, ["Category_see_also"] = 35000, ["Category_see_also/Category_pair_check"] = 35000, ["Category_see_also_if_exists"] = 72000, ["Category_see_also_if_exists_2"] = 86000, ["Category_title"] = 2400, ["Catexp"] = 7900, ["CathEncy"] = 2300, ["Catholic"] = 4100, ["Catholic_Encyclopedia"] = 5100, ["Catmain"] = 26000, ["Catmore"] = 9400, ["Cbb_link"] = 8300, ["Cbignore"] = 99000, ["Cbsb_link"] = 2100, ["Cc-by-2.5"] = 4000, ["Cc-by-3.0"] = 9000, ["Cc-by-sa-2.5"] = 2600, ["Cc-by-sa-2.5,2.0,1.0"] = 2800, ["Cc-by-sa-3.0"] = 27000, ["Cc-by-sa-3.0,2.5,2.0,1.0"] = 2300, ["Cc-by-sa-3.0-migrated"] = 25000, ["Cc-by-sa-4.0"] = 12000, ["Cc-zero"] = 4200, ["CensusAU"] = 9200, ["Census_2016_AUS"] = 7100, ["Cent"] = 5600, ["Center"] = 285000, ["Centralized_discussion"] = 5900, ["Centralized_discussion/core"] = 5900, ["Centralized_discussion/styles.css"] = 5900, ["Centre"] = 3000, ["Century"] = 2100, ["Century_name_from_decade_or_year"] = 75000, ["Century_name_from_title_decade"] = 7600, ["Century_name_from_title_year"] = 7600, ["Certification_Cite/Title"] = 29000, ["Certification_Cite/URL"] = 32000, ["Certification_Cite/archivedate"] = 5900, ["Certification_Cite/archiveurl"] = 5900, ["Certification_Cite_Ref"] = 28000, ["Certification_Table_Bottom"] = 28000, ["Certification_Table_Entry"] = 29000, ["Certification_Table_Entry/Foot"] = 27000, ["Certification_Table_Entry/Foot/helper"] = 27000, ["Certification_Table_Entry/Region"] = 29000, ["Certification_Table_Entry/Sales"] = 27000, ["Certification_Table_Entry/Sales/BelgianPeriod"] = 2100, ["Certification_Table_Entry/Sales/DanishPeriod"] = 3100, ["Certification_Table_Entry/Sales/DanishPeriodHelper1"] = 3100, ["Certification_Table_Entry/Sales/DanishPeriodHelper2"] = 3100, ["Certification_Table_Entry/Sales/GermanPeriod"] = 3600, ["Certification_Table_Entry/Sales/ItalianHelper"] = 3100, ["Certification_Table_Entry/Sales/SwedishPeriod"] = 2100, ["Certification_Table_Separator"] = 2200, ["Certification_Table_Top"] = 29000, ["Cfb_link"] = 23000, ["Cfd_result"] = 2400, ["Cfdend"] = 4000, ["Chart"] = 4600, ["Chart/end"] = 4700, ["Chart/start"] = 4600, ["Chart_bottom"] = 3300, ["Chart_top"] = 3300, ["Check_completeness_of_transclusions"] = 7200, ["Check_talk"] = 29000, ["Check_talk_wp"] = 1360000, ["Check_winner_by_scores"] = 13000, ["CheckedSockpuppet"] = 7100, ["Checked_sockpuppet"] = 18000, ["Checkedsockpuppet"] = 5300, ["Checkip"] = 12000, ["Checkuser"] = 74000, ["Checkuserblock-account"] = 14000, ["Chem"] = 5200, ["Chem/atom"] = 5200, ["Chem/link"] = 5200, ["Chem2"] = 4000, ["Chem_molar_mass"] = 17000, ["Chem_molar_mass/format"] = 17000, ["Chembox"] = 14000, ["Chembox/styles.css"] = 14000, ["Chembox_3DMet"] = 14000, ["Chembox_3DMet/format"] = 14000, ["Chembox_AllOtherNames"] = 12000, ["Chembox_AllOtherNames/format"] = 12000, ["Chembox_Appearance"] = 5900, ["Chembox_BoilingPt"] = 3700, ["Chembox_CASNo"] = 14000, ["Chembox_CASNo/format"] = 14000, ["Chembox_CalcTemperatures"] = 6600, ["Chembox_ChEBI"] = 14000, ["Chembox_ChEBI/format"] = 14000, ["Chembox_ChEMBL"] = 14000, ["Chembox_ChEMBL/format"] = 14000, ["Chembox_ChemSpiderID"] = 14000, ["Chembox_ChemSpiderID/format"] = 14000, ["Chembox_CompTox"] = 14000, ["Chembox_CompTox/format"] = 14000, ["Chembox_Datapage_check"] = 14000, ["Chembox_Density"] = 4800, ["Chembox_DrugBank"] = 14000, ["Chembox_DrugBank/format"] = 14000, ["Chembox_ECHA"] = 7500, ["Chembox_ECNumber"] = 14000, ["Chembox_ECNumber/format"] = 14000, ["Chembox_Elements"] = 13000, ["Chembox_Elements/molecular_formula"] = 17000, ["Chembox_Footer"] = 14000, ["Chembox_Footer/tracking"] = 14000, ["Chembox_GHS_(set)"] = 3400, ["Chembox_Hazards"] = 11000, ["Chembox_IUPHAR_ligand"] = 14000, ["Chembox_IUPHAR_ligand/format"] = 14000, ["Chembox_Identifiers"] = 13000, ["Chembox_InChI"] = 12000, ["Chembox_InChI/format"] = 12000, ["Chembox_Indexlist"] = 14000, ["Chembox_Jmol"] = 14000, ["Chembox_Jmol/format"] = 14000, ["Chembox_KEGG"] = 14000, ["Chembox_KEGG/format"] = 14000, ["Chembox_MeltingPt"] = 5700, ["Chembox_Properties"] = 13000, ["Chembox_PubChem"] = 14000, ["Chembox_PubChem/format"] = 14000, ["Chembox_RTECS"] = 14000, ["Chembox_RTECS/format"] = 14000, ["Chembox_Related"] = 3200, ["Chembox_SMILES"] = 13000, ["Chembox_SMILES/format"] = 13000, ["Chembox_SolubilityInWater"] = 3800, ["Chembox_UNII"] = 14000, ["Chembox_UNII/format"] = 13000, ["Chembox_headerbar"] = 14000, ["Chembox_image"] = 13000, ["Chembox_image_cell"] = 12000, ["Chembox_image_sbs"] = 13000, ["Chembox_parametercheck"] = 13000, ["Chembox_setDatarow"] = 4400, ["Chembox_setHeader"] = 4400, ["Chembox_templatePar/formatPreviewMessage"] = 14000, ["Chembox_verification"] = 7100, ["Chemicals"] = 7400, ["Chemistry"] = 3100, ["Chemspidercite"] = 11000, ["Chessgames_player"] = 3600, ["Chinese"] = 7700, ["Chr"] = 9100, ["ChristianityWikiProject"] = 5900, ["Circa"] = 59000, ["Circular_reference"] = 4300, ["Citation"] = 394000, ["Citation/make_link"] = 5800, ["Citation/styles.css"] = 46000, ["Citation_needed"] = 530000, ["Citation_needed_span"] = 3400, ["Citation_style"] = 4200, ["Cite_AV_media"] = 41000, ["Cite_AV_media_notes"] = 25000, ["Cite_Appletons'"] = 2400, ["Cite_Australian_Dictionary_of_Biography"] = 3200, ["Cite_Catholic_Encyclopedia"] = 8100, ["Cite_Colledge2006"] = 3100, ["Cite_DCB"] = 2800, ["Cite_DNB"] = 17000, ["Cite_EB1911"] = 25000, ["Cite_GNIS"] = 2300, ["Cite_Gaia_DR2"] = 2200, ["Cite_Jewish_Encyclopedia"] = 2900, ["Cite_NIE"] = 3600, ["Cite_NSW_Parliament"] = 3300, ["Cite_NSW_SHR"] = 2600, ["Cite_ODNB"] = 17000, ["Cite_Q"] = 43000, ["Cite_QHR"] = 3000, ["Cite_QPN"] = 3900, ["Cite_Rowlett"] = 2500, ["Cite_Russian_law"] = 7800, ["Cite_Ryan"] = 3200, ["Cite_Sports-Reference"] = 54000, ["Cite_USGov"] = 24000, ["Cite_WoRMS"] = 5200, ["Cite_act"] = 2400, ["Cite_arXiv"] = 4700, ["Cite_bcgnis"] = 3100, ["Cite_book"] = 1550000, ["Cite_certification"] = 32000, ["Cite_cgndb"] = 3100, ["Cite_conference"] = 15000, ["Cite_court"] = 5200, ["Cite_dictionary"] = 5500, ["Cite_document"] = 8100, ["Cite_encyclopedia"] = 198000, ["Cite_episode"] = 17000, ["Cite_gnis"] = 34000, ["Cite_interview"] = 7400, ["Cite_iucn"] = 56000, ["Cite_journal"] = 931000, ["Cite_magazine"] = 242000, ["Cite_manual"] = 2000, ["Cite_map"] = 34000, ["Cite_news"] = 1460000, ["Cite_newspaper_The_Times"] = 6500, ["Cite_patent"] = 5200, ["Cite_patent/authors"] = 4200, ["Cite_patent/core"] = 5600, ["Cite_peakbagger"] = 4300, ["Cite_podcast"] = 3500, ["Cite_press_release"] = 61000, ["Cite_report"] = 34000, ["Cite_rowlett"] = 2500, ["Cite_simbad"] = 4400, ["Cite_sports-reference"] = 59000, ["Cite_thesis"] = 29000, ["Cite_tweet"] = 33000, ["Cite_video"] = 12000, ["Cite_video_game"] = 3000, ["Cite_web"] = 4470000, ["Cite_wikisource"] = 5400, ["Cite_wikisource/make_link"] = 57000, ["Civil_navigation"] = 2300, ["Cl"] = 116000, ["Clade"] = 7300, ["Clade/styles.css"] = 7300, ["Clarify"] = 40000, ["Class"] = 9870000, ["Class/colour"] = 541000, ["Class/icon"] = 28000, ["Class_mask"] = 10100000, ["Class_mask/b"] = 343000, ["Classical"] = 6900, ["Classicon"] = 4700, ["Clc"] = 5800, ["Cleanup"] = 10000, ["Cleanup_bare_URLs"] = 35000, ["Cleanup_reorganize"] = 2500, ["Cleanup_rewrite"] = 5700, ["Clear"] = 2880000, ["Clear-left"] = 16000, ["Clear_left"] = 30000, ["Clear_right"] = 2900, ["Clerk-Note"] = 9600, ["Clerknote"] = 7300, ["Clickable_button"] = 16000, ["Clickable_button_2"] = 934000, ["Closed_access"] = 4300, ["Closed_rfc_top"] = 2100, ["Clr"] = 3800, ["Cmbox"] = 406000, ["Cn"] = 88000, ["Cnote2"] = 2200, ["Cnote2_Begin"] = 2200, ["Cnote2_End"] = 2200, ["Coat_of_arms"] = 5200, ["Cob"] = 12000, ["Code"] = 48000, ["Col-1-of-2"] = 2400, ["Col-2"] = 169000, ["Col-2-of-2"] = 2300, ["Col-3"] = 9700, ["Col-4"] = 3400, ["Col-begin"] = 210000, ["Col-break"] = 209000, ["Col-end"] = 209000, ["Col-float"] = 2600, ["Col-float-break"] = 2500, ["Col-float-end"] = 2600, ["Col-float/styles.css"] = 2600, ["Col-start"] = 21000, ["Colbegin"] = 20000, ["Colend"] = 23000, ["Collapse"] = 9500, ["Collapse_bottom"] = 51000, ["Collapse_top"] = 52000, ["Collapsebottom"] = 3800, ["Collapsetop"] = 3800, ["Collapsible_list"] = 52000, ["Collapsible_option"] = 133000, ["College"] = 8600, ["CollegePrimaryHeader"] = 5500, ["CollegePrimaryStyle"] = 94000, ["CollegeSecondaryStyle"] = 2900, ["College_Athlete_Recruit_End"] = 2800, ["College_Athlete_Recruit_Entry"] = 2900, ["College_Athlete_Recruit_Start"] = 2900, ["College_athlete_recruit_end"] = 3900, ["College_athlete_recruit_entry"] = 4100, ["College_athlete_recruit_start"] = 4100, ["College_color_list"] = 3800, ["Colon"] = 17000, ["Color"] = 463000, ["Color_box"] = 72000, ["Colorbox"] = 3600, ["Colorbull"] = 4900, ["Colored_link"] = 60000, ["Colors"] = 3400, ["Colour"] = 5800, ["Coloured_link"] = 6400, ["Column"] = 2400, ["Column/styles.css"] = 2400, ["Columns-end"] = 2100, ["Columns-list"] = 95000, ["Columns-start"] = 2100, ["Comedy"] = 2600, ["Comic_Book_DB"] = 3600, ["Comicbookdb"] = 3500, ["Comics-replaceability"] = 2900, ["Comics_infobox_sec/creator_nat"] = 2800, ["Comics_infobox_sec/formcat"] = 3200, ["Comics_infobox_sec/genre"] = 3900, ["Comics_infobox_sec/genrecat"] = 3600, ["Comics_infobox_sec/styles.css"] = 8100, ["Comicsproj"] = 28000, ["Comma_separated_entries"] = 419000, ["Comma_separated_values"] = 43000, ["Comment"] = 4900, ["Committed_identity"] = 3000, ["Committed_identity/styles.css"] = 3000, ["Commons"] = 65000, ["Commons-inline"] = 19000, ["Commons_cat"] = 48000, ["Commons_category"] = 837000, ["Commons_category-inline"] = 142000, ["Commons_category_inline"] = 5900, ["Commonscat"] = 66000, ["Commonscat-inline"] = 17000, ["Commonscat_inline"] = 2300, ["Commonscatinline"] = 7500, ["Compact_TOC"] = 6900, ["Compact_ToC"] = 5000, ["Compare"] = 4900, ["Compare_image_with_Wikidata"] = 9800, ["Composition_bar"] = 9900, ["Confirmed"] = 16000, ["Confused"] = 2700, ["Confusing"] = 2400, ["CongBio"] = 9700, ["CongLinks"] = 4500, ["Connected_contributor"] = 17000, ["Connected_contributor_(paid)"] = 6700, ["Constellation_navbox"] = 6400, ["Container"] = 10000, ["Container_cat"] = 7400, ["Container_category"] = 41000, ["Containercat"] = 2700, ["Contains_special_characters"] = 3900, ["Contains_special_characters/core"] = 3900, ["Contains_special_characters/styles.css"] = 3900, ["Content_category"] = 7600, ["Contentious_topics/list"] = 13000, ["Contentious_topics/page_restriction_editnotice_base"] = 2400, ["Contentious_topics/page_restriction_talk_notice_base"] = 3600, ["Contentious_topics/talk_notice"] = 6300, ["Context"] = 2700, ["Continent2continental"] = 16000, ["Continent_adjective_to_noun"] = 2200, ["Controversial"] = 3200, ["Convert"] = 1150000, ["Convinfobox"] = 202000, ["Convinfobox/2"] = 16000, ["Convinfobox/3"] = 118000, ["Convinfobox/pri2"] = 62000, ["Convinfobox/prisec2"] = 3200, ["Convinfobox/prisec3"] = 25000, ["Convinfobox/sec2"] = 9000, ["Coord"] = 1320000, ["Coord_missing"] = 98000, ["Coord_missing/CheckCat"] = 97000, ["Coords"] = 8100, ["Copied"] = 18000, ["Copy_edit"] = 2300, ["Copy_to_Wikimedia_Commons"] = 114000, ["Copyvios"] = 4200, ["Cospar"] = 2500, ["Cot"] = 11000, ["Count"] = 645000, ["Country2continent"] = 34000, ["Country2continental"] = 2400, ["Country2nationality"] = 335000, ["CountryPrefixThe"] = 108000, ["Country_abbreviation"] = 86000, ["Country_alias"] = 15000, ["Country_at_games_navbox"] = 2600, ["Country_at_games_navbox/below"] = 2600, ["Country_data"] = 6300, ["Country_data_AFG"] = 2200, ["Country_data_ALB"] = 6400, ["Country_data_ALG"] = 9100, ["Country_data_AND"] = 2900, ["Country_data_ANG"] = 3800, ["Country_data_ARG"] = 46000, ["Country_data_ARM"] = 7000, ["Country_data_AUS"] = 73000, ["Country_data_AUT"] = 45000, ["Country_data_AZE"] = 8600, ["Country_data_Afghanistan"] = 12000, ["Country_data_Alaska"] = 2100, ["Country_data_Albania"] = 19000, ["Country_data_Alberta"] = 3600, ["Country_data_Algeria"] = 24000, ["Country_data_American_Samoa"] = 2900, ["Country_data_Andorra"] = 7700, ["Country_data_Angola"] = 11000, ["Country_data_Anguilla"] = 2500, ["Country_data_Antigua_and_Barbuda"] = 6000, ["Country_data_Apulia"] = 7900, ["Country_data_Argentina"] = 79000, ["Country_data_Arizona"] = 2300, ["Country_data_Arkansas"] = 2000, ["Country_data_Armenia"] = 21000, ["Country_data_Aruba"] = 3600, ["Country_data_Australia"] = 124000, ["Country_data_Austria"] = 77000, ["Country_data_Azerbaijan"] = 27000, ["Country_data_BAH"] = 3800, ["Country_data_BAN"] = 3800, ["Country_data_BAR"] = 2400, ["Country_data_BEL"] = 50000, ["Country_data_BER"] = 2300, ["Country_data_BHR"] = 4400, ["Country_data_BIH"] = 12000, ["Country_data_BLR"] = 24000, ["Country_data_BOL"] = 5600, ["Country_data_BOT"] = 2200, ["Country_data_BRA"] = 56000, ["Country_data_BUL"] = 25000, ["Country_data_Bahamas"] = 9600, ["Country_data_Bahrain"] = 12000, ["Country_data_Bangladesh"] = 18000, ["Country_data_Barbados"] = 7900, ["Country_data_Belarus"] = 43000, ["Country_data_Belgium"] = 87000, ["Country_data_Belize"] = 5200, ["Country_data_Benin"] = 7100, ["Country_data_Bermuda"] = 5700, ["Country_data_Bhutan"] = 4600, ["Country_data_Bolivia"] = 14000, ["Country_data_Bosnia_and_Herzegovina"] = 28000, ["Country_data_Botswana"] = 8900, ["Country_data_Brazil"] = 100000, ["Country_data_British_Columbia"] = 3400, ["Country_data_British_Raj"] = 2200, ["Country_data_British_Virgin_Islands"] = 3200, ["Country_data_Brunei"] = 6100, ["Country_data_Bulgaria"] = 51000, ["Country_data_Burkina_Faso"] = 9900, ["Country_data_Burma"] = 2700, ["Country_data_Burundi"] = 5800, ["Country_data_CAN"] = 57000, ["Country_data_CGO"] = 2300, ["Country_data_CHE"] = 4600, ["Country_data_CHI"] = 17000, ["Country_data_CHL"] = 2000, ["Country_data_CHN"] = 40000, ["Country_data_CIV"] = 7800, ["Country_data_CMR"] = 8500, ["Country_data_COD"] = 3100, ["Country_data_COL"] = 24000, ["Country_data_CRC"] = 6500, ["Country_data_CRO"] = 32000, ["Country_data_CUB"] = 9500, ["Country_data_CYP"] = 8800, ["Country_data_CZE"] = 45000, ["Country_data_California"] = 5800, ["Country_data_Cambodia"] = 8500, ["Country_data_Cameroon"] = 17000, ["Country_data_Canada"] = 120000, ["Country_data_Cape_Verde"] = 6100, ["Country_data_Castile_and_León"] = 2000, ["Country_data_Catalonia"] = 3100, ["Country_data_Cayman_Islands"] = 4100, ["Country_data_Central_African_Republic"] = 4900, ["Country_data_Chad"] = 5400, ["Country_data_Chile"] = 39000, ["Country_data_China"] = 82000, ["Country_data_Chinese_Taipei"] = 19000, ["Country_data_Colombia"] = 45000, ["Country_data_Colorado"] = 5400, ["Country_data_Comoros"] = 4300, ["Country_data_Confederate_States_of_America"] = 3100, ["Country_data_Connecticut"] = 3200, ["Country_data_Cook_Islands"] = 3800, ["Country_data_Costa_Rica"] = 17000, ["Country_data_Croatia"] = 55000, ["Country_data_Cuba"] = 22000, ["Country_data_Curaçao"] = 3500, ["Country_data_Cyprus"] = 22000, ["Country_data_Czech_Republic"] = 80000, ["Country_data_Czechoslovakia"] = 18000, ["Country_data_DEN"] = 33000, ["Country_data_DEU"] = 8400, ["Country_data_DNK"] = 3400, ["Country_data_DOM"] = 6900, ["Country_data_Democratic_Republic_of_the_Congo"] = 12000, ["Country_data_Denmark"] = 68000, ["Country_data_Djibouti"] = 4500, ["Country_data_Dominica"] = 4200, ["Country_data_Dominican_Republic"] = 17000, ["Country_data_ECU"] = 11000, ["Country_data_EGY"] = 13000, ["Country_data_ENG"] = 45000, ["Country_data_ESA"] = 2200, ["Country_data_ESP"] = 71000, ["Country_data_EST"] = 14000, ["Country_data_ETH"] = 3300, ["Country_data_EU"] = 3600, ["Country_data_East_Germany"] = 14000, ["Country_data_East_Timor"] = 4800, ["Country_data_Ecuador"] = 24000, ["Country_data_Egypt"] = 31000, ["Country_data_El_Salvador"] = 13000, ["Country_data_Empire_of_Japan"] = 3900, ["Country_data_England"] = 95000, ["Country_data_Equatorial_Guinea"] = 5100, ["Country_data_Eritrea"] = 5200, ["Country_data_Estonia"] = 34000, ["Country_data_Eswatini"] = 4800, ["Country_data_Ethiopia"] = 12000, ["Country_data_Europe"] = 2400, ["Country_data_European_Union"] = 7200, ["Country_data_FIJ"] = 3700, ["Country_data_FIN"] = 33000, ["Country_data_FRA"] = 96000, ["Country_data_FRG"] = 14000, ["Country_data_FR_Yugoslavia"] = 4000, ["Country_data_Faroe_Islands"] = 5300, ["Country_data_Federated_States_of_Micronesia"] = 3000, ["Country_data_Fiji"] = 12000, ["Country_data_Finland"] = 67000, ["Country_data_Florida"] = 6400, ["Country_data_France"] = 190000, ["Country_data_French_Guiana"] = 2100, ["Country_data_French_Polynesia"] = 3700, ["Country_data_GAB"] = 2300, ["Country_data_GBR"] = 53000, ["Country_data_GDR"] = 8000, ["Country_data_GEO"] = 13000, ["Country_data_GER"] = 80000, ["Country_data_GHA"] = 9500, ["Country_data_GRE"] = 25000, ["Country_data_GUA"] = 4900, ["Country_data_GUI"] = 3100, ["Country_data_GUY"] = 2300, ["Country_data_Gabon"] = 7300, ["Country_data_Gambia"] = 6500, ["Country_data_Georgia"] = 8100, ["Country_data_Georgia_(U.S._state)"] = 2800, ["Country_data_Georgia_(country)"] = 28000, ["Country_data_German_Empire"] = 5300, ["Country_data_Germany"] = 148000, ["Country_data_Ghana"] = 23000, ["Country_data_Gibraltar"] = 4800, ["Country_data_Great_Britain"] = 72000, ["Country_data_Greece"] = 56000, ["Country_data_Greenland"] = 2800, ["Country_data_Grenada"] = 5100, ["Country_data_Guadeloupe"] = 2800, ["Country_data_Guam"] = 4700, ["Country_data_Guatemala"] = 13000, ["Country_data_Guernsey"] = 2100, ["Country_data_Guinea"] = 8200, ["Country_data_Guinea-Bissau"] = 4900, ["Country_data_Guyana"] = 7400, ["Country_data_HAI"] = 3100, ["Country_data_HKG"] = 13000, ["Country_data_HON"] = 4300, ["Country_data_HUN"] = 36000, ["Country_data_Haiti"] = 8600, ["Country_data_Honduras"] = 12000, ["Country_data_Hong_Kong"] = 25000, ["Country_data_Hungary"] = 69000, ["Country_data_IDN"] = 4800, ["Country_data_INA"] = 10000, ["Country_data_IND"] = 29000, ["Country_data_IRE"] = 10000, ["Country_data_IRI"] = 5300, ["Country_data_IRL"] = 20000, ["Country_data_IRN"] = 6100, ["Country_data_IRQ"] = 4000, ["Country_data_ISL"] = 8300, ["Country_data_ISR"] = 20000, ["Country_data_ITA"] = 84000, ["Country_data_Iceland"] = 22000, ["Country_data_Idaho"] = 2000, ["Country_data_Illinois"] = 4400, ["Country_data_India"] = 108000, ["Country_data_Indiana"] = 2800, ["Country_data_Indonesia"] = 36000, ["Country_data_Iowa"] = 3000, ["Country_data_Iran"] = 90000, ["Country_data_Iraq"] = 14000, ["Country_data_Ireland"] = 34000, ["Country_data_Isle_of_Man"] = 2800, ["Country_data_Israel"] = 45000, ["Country_data_Italy"] = 142000, ["Country_data_Ivory_Coast"] = 17000, ["Country_data_JAM"] = 9200, ["Country_data_JOR"] = 3900, ["Country_data_JP"] = 8200, ["Country_data_JPN"] = 58000, ["Country_data_Jamaica"] = 20000, ["Country_data_Japan"] = 116000, ["Country_data_Jersey"] = 2500, ["Country_data_Jordan"] = 12000, ["Country_data_KAZ"] = 19000, ["Country_data_KEN"] = 7000, ["Country_data_KGZ"] = 3600, ["Country_data_KOR"] = 30000, ["Country_data_KOS"] = 2200, ["Country_data_KSA"] = 5700, ["Country_data_KUW"] = 4000, ["Country_data_Kazakhstan"] = 33000, ["Country_data_Kenya"] = 19000, ["Country_data_Kingdom_of_France"] = 2100, ["Country_data_Kingdom_of_Great_Britain"] = 4800, ["Country_data_Kingdom_of_Italy"] = 4000, ["Country_data_Kiribati"] = 2900, ["Country_data_Kosovo"] = 8300, ["Country_data_Kuwait"] = 11000, ["Country_data_Kyrgyzstan"] = 9100, ["Country_data_LAT"] = 14000, ["Country_data_LBN"] = 2200, ["Country_data_LIB"] = 2500, ["Country_data_LIE"] = 3100, ["Country_data_LIT"] = 3000, ["Country_data_LTU"] = 12000, ["Country_data_LUX"] = 10000, ["Country_data_LVA"] = 2500, ["Country_data_Laos"] = 7300, ["Country_data_Latvia"] = 31000, ["Country_data_Lebanon"] = 15000, ["Country_data_Lesotho"] = 5000, ["Country_data_Liberia"] = 7100, ["Country_data_Libya"] = 8400, ["Country_data_Liechtenstein"] = 7700, ["Country_data_Lithuania"] = 31000, ["Country_data_Luxembourg"] = 23000, ["Country_data_MAC"] = 2400, ["Country_data_MAR"] = 12000, ["Country_data_MAS"] = 11000, ["Country_data_MDA"] = 7400, ["Country_data_MEX"] = 29000, ["Country_data_MGL"] = 2900, ["Country_data_MKD"] = 7200, ["Country_data_MLI"] = 4200, ["Country_data_MLT"] = 5400, ["Country_data_MNE"] = 7600, ["Country_data_MON"] = 3600, ["Country_data_MOZ"] = 2100, ["Country_data_MYA"] = 2900, ["Country_data_MYS"] = 3600, ["Country_data_Macau"] = 6200, ["Country_data_Macedonia"] = 4900, ["Country_data_Madagascar"] = 8900, ["Country_data_Malawi"] = 5500, ["Country_data_Malaysia"] = 35000, ["Country_data_Maldives"] = 5900, ["Country_data_Mali"] = 12000, ["Country_data_Malta"] = 17000, ["Country_data_Manitoba"] = 2500, ["Country_data_Marshall_Islands"] = 3600, ["Country_data_Martinique"] = 2800, ["Country_data_Maryland"] = 3000, ["Country_data_Massachusetts"] = 2900, ["Country_data_Mauritania"] = 5600, ["Country_data_Mauritius"] = 7700, ["Country_data_Mexico"] = 65000, ["Country_data_Michigan"] = 4200, ["Country_data_Minnesota"] = 3700, ["Country_data_Missouri"] = 2000, ["Country_data_Moldova"] = 19000, ["Country_data_Monaco"] = 9800, ["Country_data_Mongolia"] = 9500, ["Country_data_Montana"] = 2100, ["Country_data_Montenegro"] = 18000, ["Country_data_Montserrat"] = 2500, ["Country_data_Morocco"] = 27000, ["Country_data_Mozambique"] = 7100, ["Country_data_Myanmar"] = 13000, ["Country_data_NAM"] = 3300, ["Country_data_NED"] = 59000, ["Country_data_NEP"] = 2800, ["Country_data_NGA"] = 7900, ["Country_data_NGR"] = 7800, ["Country_data_NIR"] = 9600, ["Country_data_NLD"] = 6000, ["Country_data_NOR"] = 29000, ["Country_data_NZ"] = 3200, ["Country_data_NZL"] = 31000, ["Country_data_Namibia"] = 9600, ["Country_data_Nauru"] = 2500, ["Country_data_Nazi_Germany"] = 9600, ["Country_data_Nepal"] = 17000, ["Country_data_Netherlands"] = 111000, ["Country_data_Netherlands_Antilles"] = 2300, ["Country_data_New_Brunswick"] = 2500, ["Country_data_New_Caledonia"] = 3300, ["Country_data_New_Jersey"] = 4100, ["Country_data_New_South_Wales"] = 5700, ["Country_data_New_York"] = 4800, ["Country_data_New_York_(state)"] = 6700, ["Country_data_New_Zealand"] = 65000, ["Country_data_Newfoundland_and_Labrador"] = 2300, ["Country_data_Nicaragua"] = 8200, ["Country_data_Niger"] = 5700, ["Country_data_Nigeria"] = 32000, ["Country_data_North_Carolina"] = 3500, ["Country_data_North_Korea"] = 13000, ["Country_data_North_Macedonia"] = 17000, ["Country_data_Northern_Ireland"] = 15000, ["Country_data_Northern_Mariana_Islands"] = 2800, ["Country_data_Norway"] = 71000, ["Country_data_Nova_Scotia"] = 2300, ["Country_data_OMA"] = 2600, ["Country_data_Ohio"] = 4800, ["Country_data_Oman"] = 8500, ["Country_data_Ontario"] = 3800, ["Country_data_Ottoman_Empire"] = 2500, ["Country_data_PAK"] = 7900, ["Country_data_PAN"] = 5600, ["Country_data_PAR"] = 9800, ["Country_data_PER"] = 12000, ["Country_data_PHI"] = 11000, ["Country_data_PHL"] = 2500, ["Country_data_PNG"] = 2600, ["Country_data_POL"] = 48000, ["Country_data_POR"] = 30000, ["Country_data_PRC"] = 2100, ["Country_data_PRK"] = 4600, ["Country_data_PRT"] = 2800, ["Country_data_PUR"] = 7000, ["Country_data_Pakistan"] = 28000, ["Country_data_Palau"] = 2900, ["Country_data_Palestine"] = 6600, ["Country_data_Panama"] = 16000, ["Country_data_Papua_New_Guinea"] = 7800, ["Country_data_Paraguay"] = 20000, ["Country_data_Pennsylvania"] = 3700, ["Country_data_People's_Republic_of_China"] = 3300, ["Country_data_Peru"] = 30000, ["Country_data_Philippines"] = 34000, ["Country_data_Poland"] = 148000, ["Country_data_Portugal"] = 67000, ["Country_data_Prussia"] = 2600, ["Country_data_Puerto_Rico"] = 17000, ["Country_data_QAT"] = 7500, ["Country_data_Qatar"] = 17000, ["Country_data_Quebec"] = 4200, ["Country_data_ROM"] = 13000, ["Country_data_ROU"] = 26000, ["Country_data_RSA"] = 30000, ["Country_data_RUS"] = 62000, ["Country_data_Republic_of_China"] = 5600, ["Country_data_Republic_of_Ireland"] = 25000, ["Country_data_Republic_of_the_Congo"] = 7400, ["Country_data_Romania"] = 67000, ["Country_data_Russia"] = 113000, ["Country_data_Russian_Empire"] = 4800, ["Country_data_Rwanda"] = 7400, ["Country_data_SAM"] = 3000, ["Country_data_SCG"] = 3100, ["Country_data_SCO"] = 25000, ["Country_data_SEN"] = 7700, ["Country_data_SER"] = 3500, ["Country_data_SGP"] = 2500, ["Country_data_SIN"] = 6900, ["Country_data_SLO"] = 19000, ["Country_data_SLV"] = 2900, ["Country_data_SMR"] = 3000, ["Country_data_SPA"] = 4500, ["Country_data_SRB"] = 26000, ["Country_data_SRI"] = 4600, ["Country_data_SUI"] = 41000, ["Country_data_SUR"] = 2000, ["Country_data_SVK"] = 28000, ["Country_data_SVN"] = 6600, ["Country_data_SWE"] = 55000, ["Country_data_SWI"] = 4600, ["Country_data_SYR"] = 3500, ["Country_data_Saint_Kitts_and_Nevis"] = 4700, ["Country_data_Saint_Lucia"] = 4800, ["Country_data_Saint_Vincent_and_the_Grenadines"] = 4800, ["Country_data_Samoa"] = 7600, ["Country_data_San_Marino"] = 8200, ["Country_data_Saskatchewan"] = 2900, ["Country_data_Saudi_Arabia"] = 18000, ["Country_data_Scotland"] = 50000, ["Country_data_Senegal"] = 17000, ["Country_data_Serbia"] = 53000, ["Country_data_Serbia_and_Montenegro"] = 5000, ["Country_data_Seychelles"] = 5400, ["Country_data_Sierra_Leone"] = 7100, ["Country_data_Singapore"] = 26000, ["Country_data_Slovakia"] = 50000, ["Country_data_Slovenia"] = 42000, ["Country_data_Solomon_Islands"] = 4600, ["Country_data_Somalia"] = 6000, ["Country_data_South_Africa"] = 68000, ["Country_data_South_Carolina"] = 3300, ["Country_data_South_Korea"] = 65000, ["Country_data_South_Sudan"] = 3900, ["Country_data_South_Vietnam"] = 2100, ["Country_data_Soviet_Union"] = 35000, ["Country_data_Spain"] = 131000, ["Country_data_Sri_Lanka"] = 19000, ["Country_data_Sudan"] = 7800, ["Country_data_Suriname"] = 6400, ["Country_data_Sweden"] = 99000, ["Country_data_Switzerland"] = 81000, ["Country_data_Syria"] = 15000, ["Country_data_São_Tomé_and_Príncipe"] = 3300, ["Country_data_TAN"] = 2400, ["Country_data_TCH"] = 10000, ["Country_data_THA"] = 20000, ["Country_data_TJK"] = 2500, ["Country_data_TKM"] = 2700, ["Country_data_TPE"] = 15000, ["Country_data_TRI"] = 4700, ["Country_data_TUN"] = 10000, ["Country_data_TUR"] = 27000, ["Country_data_Taiwan"] = 13000, ["Country_data_Tajikistan"] = 8800, ["Country_data_Tanzania"] = 12000, ["Country_data_Texas"] = 5200, ["Country_data_Thailand"] = 43000, ["Country_data_Togo"] = 6700, ["Country_data_Tonga"] = 6300, ["Country_data_Trinidad_and_Tobago"] = 14000, ["Country_data_Tunisia"] = 21000, ["Country_data_Turkey"] = 76000, ["Country_data_Turkmenistan"] = 7700, ["Country_data_Turks_and_Caicos_Islands"] = 2600, ["Country_data_Tuvalu"] = 2800, ["Country_data_U.S."] = 2100, ["Country_data_U.S._Virgin_Islands"] = 4800, ["Country_data_UAE"] = 9000, ["Country_data_UGA"] = 3900, ["Country_data_UK"] = 18000, ["Country_data_UKGBI"] = 3100, ["Country_data_UKR"] = 36000, ["Country_data_URS"] = 14000, ["Country_data_URU"] = 15000, ["Country_data_US"] = 4800, ["Country_data_USA"] = 130000, ["Country_data_USSR"] = 4500, ["Country_data_UZB"] = 11000, ["Country_data_Uganda"] = 13000, ["Country_data_Ukraine"] = 71000, ["Country_data_United_Arab_Emirates"] = 19000, ["Country_data_United_Kingdom"] = 88000, ["Country_data_United_Kingdom_of_Great_Britain_and_Ireland"] = 4400, ["Country_data_United_Nations"] = 4000, ["Country_data_United_States"] = 279000, ["Country_data_United_States_of_America"] = 5000, ["Country_data_Uruguay"] = 29000, ["Country_data_Uzbekistan"] = 20000, ["Country_data_VEN"] = 16000, ["Country_data_VIE"] = 6100, ["Country_data_Vanuatu"] = 5000, ["Country_data_Vatican_City"] = 2300, ["Country_data_Venezuela"] = 32000, ["Country_data_Vietnam"] = 22000, ["Country_data_Virginia"] = 2800, ["Country_data_WAL"] = 17000, ["Country_data_Wales"] = 33000, ["Country_data_Washington"] = 3400, ["Country_data_Washington,_D.C."] = 2200, ["Country_data_Washington_(state)"] = 3700, ["Country_data_West_Germany"] = 23000, ["Country_data_West_Indies"] = 2600, ["Country_data_Wisconsin"] = 5200, ["Country_data_YUG"] = 9500, ["Country_data_Yemen"] = 7600, ["Country_data_Yugoslavia"] = 17000, ["Country_data_ZAF"] = 4500, ["Country_data_ZAM"] = 3100, ["Country_data_ZIM"] = 8100, ["Country_data_Zambia"] = 9300, ["Country_data_Zimbabwe"] = 17000, ["Country_flagbio"] = 26000, ["Country_name"] = 23000, ["Country_showdata"] = 6000, ["Country_topics"] = 22000, ["County"] = 7400, ["County_(judet)_of_Romania"] = 3300, ["Course_assignment"] = 4200, ["Course_details"] = 6000, ["Course_instructor"] = 2400, ["Cquote"] = 37000, ["Cr"] = 4200, ["Cr-rt"] = 2000, ["Create_taxonomy/link"] = 104000, ["Cref2"] = 2200, ["Cricinfo"] = 24000, ["Cricketarchive"] = 3000, ["Crime_opentask"] = 47000, ["Croatian_Census_2011"] = 2100, ["Cross"] = 3100, ["Crossreference"] = 2400, ["Crossreference/styles.css"] = 2400, ["Csv"] = 3000, ["Ct"] = 12000, ["Curlie"] = 6800, ["Currency"] = 3400, ["Current_events"] = 8100, ["Current_events/styles.css"] = 8100, ["Currentdate"] = 22000, ["Cvt"] = 99000, ["Cycling_Archives"] = 4300, ["Cycling_archives"] = 2600, ["Cycling_data_LTS"] = 2000, ["Cycling_team_link"] = 12000, ["Module:CFB_schedule"] = 25000, ["Module:CallAssert"] = 237000, ["Module:CanElecResTopTest"] = 5600, ["Module:CanadaByProvinceCatNav"] = 9800, ["Module:Cat_main"] = 182000, ["Module:Catalog_lookup_link"] = 510000, ["Module:Category_described_in_year"] = 5700, ["Module:Category_described_in_year/conf"] = 5700, ["Module:Category_handler"] = 4390000, ["Module:Category_handler/blacklist"] = 4390000, ["Module:Category_handler/config"] = 4390000, ["Module:Category_handler/data"] = 4390000, ["Module:Category_handler/shared"] = 4390000, ["Module:Category_more_if_exists"] = 45000, ["Module:Category_pair"] = 6000, ["Module:Category_see_also"] = 35000, ["Module:Celestial_object_quadrangle"] = 2400, ["Module:Check_DYK_hook"] = 113000, ["Module:Check_for_clobbered_parameters"] = 1190000, ["Module:Check_for_deprecated_parameters"] = 54000, ["Module:Check_for_unknown_parameters"] = 13500000, ["Module:Check_isxn"] = 479000, ["Module:Check_winner_by_scores"] = 13000, ["Module:Checkuser"] = 74000, ["Module:Chem2"] = 4000, ["Module:Chem2/styles.css"] = 4000, ["Module:Citation/CS1"] = 5480000, ["Module:Citation/CS1/COinS"] = 5480000, ["Module:Citation/CS1/Configuration"] = 5480000, ["Module:Citation/CS1/Date_validation"] = 5480000, ["Module:Citation/CS1/Identifiers"] = 5480000, ["Module:Citation/CS1/Suggestions"] = 26000, ["Module:Citation/CS1/Utilities"] = 5480000, ["Module:Citation/CS1/Whitelist"] = 5480000, ["Module:Citation/CS1/styles.css"] = 5630000, ["Module:Cite_Q"] = 43000, ["Module:Cite_iucn"] = 56000, ["Module:Cite_tweet"] = 33000, ["Module:Cite_web"] = 37000, ["Module:Clade"] = 7300, ["Module:Class"] = 9930000, ["Module:Class/definition.json"] = 9930000, ["Module:Class/styles.css"] = 9870000, ["Module:Clickable_button_2"] = 934000, ["Module:Collapsible_list"] = 53000, ["Module:College_color"] = 125000, ["Module:College_color/data"] = 125000, ["Module:Color_contrast"] = 494000, ["Module:Color_contrast/colors"] = 497000, ["Module:Commons_link"] = 250000, ["Module:Complex_date"] = 65000, ["Module:Convert"] = 1210000, ["Module:Convert/data"] = 1210000, ["Module:Convert/helper"] = 7700, ["Module:Convert/text"] = 1210000, ["Module:Convert/wikidata"] = 3300, ["Module:Convert/wikidata/data"] = 3300, ["Module:ConvertNumeric"] = 14000, ["Module:Convert_character_width"] = 2800, ["Module:Convert_character_width/data"] = 2800, ["Module:Coordinates"] = 1320000, ["Module:Coordinates/styles.css"] = 1320000, ["Module:Copied"] = 18000, ["Module:CountryAdjectiveDemonym"] = 47000, ["Module:CountryAdjectiveDemonym/Adjectives"] = 47000, ["Module:CountryAdjectiveDemonym/Demonyms"] = 47000, ["Module:CountryAdjectiveDemonym/The"] = 47000, ["Module:CountryData"] = 143000, ["Module:CountryData/cacheA"] = 11000, ["Module:CountryData/cacheB"] = 8200, ["Module:CountryData/cacheC"] = 12000, ["Module:CountryData/cacheD"] = 4400, ["Module:CountryData/cacheE"] = 2700, ["Module:CountryData/cacheF"] = 2600, ["Module:CountryData/cacheG"] = 2600, ["Module:CountryData/summary"] = 143000, ["Module:Country_adjective"] = 4200, ["Module:Country_alias"] = 51000, ["Module:Country_alias/data"] = 51000, ["Module:Currency"] = 3500, ["Module:Currency/Presentation"] = 3500, }

title: Module:Transclusion count/data/C


text:
Module:Check for unknown parameters/doc

This module may be appended to a template to check for uses of unknown parameters. Unlike many other modules, this module is not implemented by a template.

Basic usage
or to sort the entries in the tracking category by parameter with a preview error message

or for an explicit red error message

Here,,  , ...,  , are the known parameters. Unnamed (positional) parameters can be added too:. Any parameter which is used, but not on this list, will cause the module to return whatever is passed with the  parameter. The  keyword, if used, will be changed to the name of the parameter. This is useful for either sorting the entries in a tracking category, or for provide more explicit information.

By default, the module makes no distinction between a defined-but-blank parameter and a non-blank parameter. That is, both unlisted x and foo are reported. To only track non-blank parameters use 1.

By default, the module ignores blank positional parameters. That is, an unlisted 2 is ignored. To include blank positional parameters in the tracking use 1.

Lua patterns
This module supports Lua patterns (similar to regular expressions), which are useful when there are many known parameters which use a systematic pattern. For example, template:infobox3cols uses to match all parameters of the form,  ,  ,  ,  ,  , ...,  , where NUM is a string of digits.
 * regexp1 = header[%d][%d]*
 * regexp2 = label[%d][%d]*
 * regexp3 = data[%d][%d]*[abc]?
 * regexp4 = class[%d][%d]*[abc]?
 * regexp5 = rowclass[%d][%d]*
 * regexp6 = rowstyle[%d][%d]*
 * regexp7 = rowcellstyle[%d][%d]*

Call from within Lua code
See the end of Module:Rugby box for a simple example or Module:Infobox3cols or Module:Flag for more complicated examples.

title: Module:Check for unknown parameters/doc


text:
Piano Solo Music: An Encyclopedia/Composers born in the 18th century/Field, John

Compositions for piano solo by John Field

 * Air russe varié [YouTube]
 * Andante in C minor [YouTube]
 * Andante inédit [YouTube]
 * Anglaise in E flat major [YouTube]
 * Danse des ours [YouTube]
 * Exercise in A flat major [YouTube]
 * Exercice modulé sur tous les tons majeurs et mineurs [YouTube]
 * Fantasia "Guardami un poco", op. 3 [YouTube]
 * Fantasia "Ah! Quel dommage" in G major [YouTube]
 * Fantasia "We met" in G major [YouTube]
 * Fantasia "In the garden" in A minor [YouTube]
 * Grand pastorale in E major [YouTube]
 * Grande valse en forme de rondeau [YouTube]
 * Largo in C minor [YouTube]
 * Marche triomphale [YouTube]
 * Nocturnes 1 - 18 [YouTube]
 * Piano sonata no. 1, op. 1 no. 2, H.8 [YouTube]
 * Piano sonata no. 2, op. 1 no. 2, H.8 [YouTube]
 * Piano sonata no. 3, op. 1 no. 2, H.8 [YouTube]
 * Piano sonata no. 4 in B major [YouTube]
 * Polonaise in E flat major [YouTube]
 * Prelude in C minor [YouTube]
 * Quadrille in E flat major [YouTube]
 * Rondo in A flat major [YouTube]
 * Rondo in A major [YouTube]
 * Rondo in A major "Favorite Hornpipe", H.2 [YouTube]
 * Rondo in B major "Speed the plough" [YouTube]
 * Rondo in C major, H.3 [YouTube]
 * Rondo in G major [YouTube]
 * Rondo in G major "Slave, bear the sparkling goblet", H.5 [YouTube]
 * Rondo in G major "The two slaves dance", H.6 [YouTube]
 * Rondo in E major "Come again, come again" [YouTube]
 * Rondo in C major "Go to the devil and shake yourself" [YouTube]
 * Rondo from piano concerto no. 1 [YouTube]
 * Rondo from piano concerto no. 2 [YouTube]
 * Rondo from piano concerto no. 3 [YouTube]
 * Rondo from piano concerto no. 4 [YouTube]
 * Rondo from piano concerto no. 5 [YouTube]
 * Rondoletto in E flat major [YouTube]
 * Sehnsuchts-Walzer, H. 51 [YouTube]
 * Siciliano, H.28 [YouTube]
 * Variations in A major on "Fal la la", H.1 [YouTube]
 * Variations in A minor on "Air du bon Roi Henry IV" [YouTube]
 * Variations in C major on "Since then I'm doom`d", H.4 [YouTube]
 * Variations in C major on "Logie of Buchan", H.7 [YouTube]
 * Variations in B flat major "Within a mile of Edinboro town" [YouTube]
 * Variations on a Russian aria "Kamarinskaya" in B flat major [YouTube]
 * Variations on a Russian folk song [YouTube]
 * Walzer in A major [YouTube]
 * Walzer in A flat major [YouTube]
 * Walzer in G major [YouTube]


text:
Cookbook:Basin Ki Kadi (Sindhi Chickpea Flour Curry)

Ingredients

 * Oil
 * 1 tbsp cumin seeds and mustard seeds
 * Curry leaves
 * 4–5 pieces finely-chopped chiles
 * 200 g besan (chickpea flour)
 * 500 ml water
 * Finely-chopped coriander leaves
 * 3 average sized tomatoes, chopped
 * Salt
 * Red chile powder
 * Turmeric

Procedure

 * 1) Heat oil in a skillet, and add the cumin seeds, mustard seeds, curry leaves, and chiles. Fry for a few minutes.
 * 2) Stir in the besan. Cook, stirring, until the besan turns golden.
 * 3) Mix in the water, and continue stirring for at least 5 minutes.
 * 4) Mix in the coriander leaves, tomatoes, salt, chili powder, and turmeric. Simmer until thick.
 * 5) Serve hot with white rice and fried potatoes.


text:
Cookbook:Seviyan Ji Khirni (Sindhi Vermicelli Pudding)

Ingredients

 * 1 tbsp ghee
 * 3 tbsp South Asian vermicelli (short, fine wheat noodles)
 * 2 litres milk
 * 10 cardamom pods
 * 20 strands saffron
 * 2 cups white granulated sugar
 * 10 almonds

Procedure

 * 1) Heat ghee in a skillet. Add vermicelli, and fry over low heat until golden brown. Remove from the heat.
 * 2) Bring the milk to a boil. Add vermicelli, cardamom, and saffron. Simmer until the vermicelli are tender and the milk is reduced to a little more than half the original volume.
 * 3) Mix in the sugar, and boil for 5–7 minutes.
 * 4) Serve, garnished with almonds.


text:
Cookbook:Sai Bhaji (Sindhi Vegetable Curry)

Ingredients

 * 150 g dried chana dal
 * 1 tbsp minced ginger
 * 3–4 chiles, chopped
 * Oil
 * 3 small bunches fenugreek leaves, finely chopped
 * 2 bunches spinach, finely chopped
 * Small amount of soy leaves, finely chopped
 * 2 average-size tomatoes, chopped
 * 2 eggplants, chopped
 * 2 potatoes, chopped
 * 8–10 ea. okra
 * 50 cloves garlic, minced
 * Salt to taste
 * 2 pinches turmeric powder
 * 1 tbsp red chile powder

Procedure

 * 1) Soak the chana dal overnight. Drain and transfer dal to a pot.
 * 2) Add the ginger and chiles to the dal. Cover with water, and boil for at least 5 minutes until tender. Drain.
 * 3) Heat the oil in a pan. Add the fenugreek leaves, spinach, soy leaves, tomatoes, eggplant, potatoes, and okra. Cook until the greens are well-wilted.
 * 4) Fry the garlic in some oil until golden. Stir in the salt, turmeric, and chile powder.
 * 5) Stir the garlic and dal into the vegetables.
 * 6) Serve hot.


text:
Cookbook:Sindhi Fried Potatoes

Ingredients

 * Large potatoes, peeled and cubed
 * Vegetable oil
 * 2 pinches salt
 * 2 pinches red chile powder
 * 2 pinches coriander powder
 * 2 pinches ground pepper

Procedure

 * 1) Heat enough oil in a pot for deep frying.
 * 2) Add the potatoes and deep fry until golden brown.
 * 3) Remove potatoes from the oil, and place on paper towels to drain.
 * 4) Transfer potatoes to a serving plate. Sprinkle with salt, chili powder, coriander, and pepper.
 * 5) Serve hot.

title: Cookbook:Sindhi Fried Potatoes


text:
Cookbook:Plain Lassi

Ingredients

 * Curd (thick plain "natural" yogurt)
 * Sugar
 * Cold water

Procedure

 * 1) Whip the curd in a bowl until loosened.
 * 2) Mix in a quarter as much water as curd.
 * 3) Sweeten with sugar to taste.
 * 4) Serve cold.

title: Cookbook:Plain Lassi


text:
Cookbook:Onion Pakoras

Ingredients

 * Besan (chickpea flour)
 * Oil
 * Green chiles, finely chopped
 * Ginger, finely chopped
 * Salt to taste
 * Red chile powder
 * 1 pinch pepper
 * Onion, sliced into half-rings

Procedure

 * 1) Combine the besan, oil, chiles, ginger, salt, chili powder, pepper, and onion in a bowl.
 * 2) Mix in enough water to make a thick batter.
 * 3) Heat enough oil in a pan for shallow- or deep frying.
 * 4) Spoon dollops of batter into the hot oil, and fry until golden brown.
 * 5) Serve with chutney.

title: Cookbook:Onion Pakoras


text:
Cookbook:Potato Pakora

Ingredients

 * Besan (chickpea flour)
 * Oil
 * Green chiles, finely chopped
 * Ginger, finely chopped
 * Salt to taste
 * Red chile powder
 * 1 pinch pepper
 * Potatoes, peeled and finely chopped

Procedure

 * 1) Combine the besan, oil, chiles, ginger, salt, chili powder, pepper, and onion in a bowl.
 * 2) Mix in enough water to make a thick batter.
 * 3) Heat enough oil in a pan for shallow- or deep frying.
 * 4) Spoon dollops of batter into the hot oil, and fry until golden brown.
 * 5) Serve hot with coriander chutney.

title: Cookbook:Potato Pakora


text:
Cookbook:Spinach Pakoras

Ingredients

 * Besan (chickpea flour)
 * Oil
 * Green chiles, finely chopped
 * Ginger, finely chopped
 * Salt to taste
 * Red chile powder
 * 1 pinch pepper
 * Spinach, finely-chopped

Procedure

 * 1) Combine the besan, oil, chiles, ginger, salt, chili powder, pepper, and spinach in a bowl.
 * 2) Mix in enough water to make a thick batter.
 * 3) Heat enough oil in a pan for shallow- or deep frying.
 * 4) Spoon dollops of batter into the hot oil, and fry until golden brown.
 * 5) Serve with coriander chutney.

title: Cookbook:Spinach Pakoras


text:
Cookbook:Sindhi Pulao

Ingredients

 * Oil
 * 5 medium onions, finely chopped
 * 3–5 green chiles, finely chopped
 * Curry leaves
 * 7–10 cashews
 * 1 tbsp garam masala
 * 1 tbsp pepper
 * 1 tsp red chile powder
 * 1 bay leaf
 * Salt to taste
 * 2 cups uncooked basmati rice

Procedure

 * 1) Heat some oil in a pot, and add the onions. Fry until dark brown.
 * 2) Add the chiles, curry leaves, cashews, garam masala, pepper, chili powder, bay leaf, and salt.
 * 3) Add the rice and enough water to cook the rice. Simmer uncovered until all the water is absorbed.
 * 4) Serve hot.

title: Cookbook:Sindhi Pulao