Asreported here in July, India'sAsian News International (ANI) has brought the Wikimedia Foundation to court. The allegation is of publishing defamatory content about ANI, at the English Wikipedia article, whichstated at that time that they had "been accused of having served as a propaganda tool for the incumbentcentral government, distributing materials from a vast network offake news websites, and misreporting events on multiple occasions". The Foundation is now being compelled by the Indian court to reveal personal information of some editors who have edited the article, according toLivemint(report) andThe Hindu(report). The next hearing will be on 25 October 2024. Wikipedia's internal consensus of ANI's suitability as a citable source for articles (as inthis 2021 discussion andWP:RSPANI) generally holds it to be somewhere between marginally reliable and generally unreliable for general reporting, prudent to give in-text attribution for potentially contentious claims, and generally unreliable in its coverage of domestic and international politics (and other topics that the government of India has a stake in). –rs

In European courts, on the other hand, things have been going better for the Wikimedia Foundation lately. In the UK, British-born Swiss lawyerMatthew Parish sued the WMF forlibel, because the article about him (correctly) noted his legal issues; however, the casehas been dismissed byHigh Court judgeKaren Steyn.

And as highlighted byTechdirt ("The Wikimedia Foundation Successfully Sees Off Another SLAPP Suit, But More Protection Is Needed Globally"), WMF recently reported another legal victory in Germany ("Wikimedia Foundation defeats gambling magnate’s lawsuit in Germany"). The Foundation characterized it as having "had all the hallmarks of an illegitimate'SLAPP' lawsuit: a strategic lawsuit against public participation. SLAPPs are lawsuits designed to force organizations and individuals to remain silent on legitimate matters of public interest. [...]The [German] Wikipedia article in question names Mladen Pavlovic as one of three co-founders ofTipico, a major European gambling company headquartered inMalta." According to the WMF, this was well-sourced public information. Yet "Pavlovic engaged a reputable German law firm to threaten the Foundation with legal action unless we agreed to censor the Wikipedia article. After consulting members of the German Wikipedia community, we refused the lawyers’ demand." Pavlovic then filed a lawsuit which "was especially intensive for our team because of the unusual number of legal briefs to which we were asked to reply. [...] These usually repeated earlier arguments, and introduced—in our opinion—increasingly thin or irrelevant new ones." As summarized by Techdirt, "that approach seems a conscious attempt to deplete Wikimedia’s limited financial resources [for legal defense], increasingly under strain" from what the WMF blog post describes as a changing legal environment:

The Foundation’s legal team now also has to deal with a wave of new and very demanding “online safety” laws across the world: for example, theEU Digital Services Act (DSA) and theUK Online Safety Act. These conditions force us to be as efficient, creative, and effective as possible, including in lawsuits like this one.

These laws may not have directly affected Pavlovic's lawsuit yet, as it predates the DSA. However, according to WMF, it fits a resource-draining pattern: "The Foundation faces several SLAPP-like cases each year" (citing examples including the still unresolvedCaesar DePaço lawsuit in Portugal, seepreviousSignpost coverage). The Foundation's post ends with a call for anti-SLAPP reform (about which, according to Techdirt, "Some progress has already been made" in the EU and UK). However, it reiterates that insufficient anti-SLAPP protection is only part of the legal challenges affecting Wikimedia projects, briefly noting other concerning developments:

Privacy-infringing laws likeFrance’s data retention law, and emergingonline identity requirements, together withlaws that give government authorities insufficiently regulated powers to order content takedowns, are also a significant issue.

–H

Wikipediabeat reporterStephen Harrison, who is best known for his articles inSlate, has recently been busy promoting his debut novel,The Editors, focused on a fictionalized version of the platform (named "Infopendium") that is suddenly caught up in globalcyberwarfare during theCOVID-19 pandemic — see previous coverage from theSignposthere andhere.

Now, though, he has writtenan article forThe Guardian detailing his view on the future of Wikipedia, which is subtitled "The world's most important knowledge platform needs young editors to rescue it from chatbots – and its own tired practices". Harrison says Wikipedia is currently facing an "existential crisis" due to the emergence ofAI applications andlarge language models, whichcould potentially undermine the platform's visibility. According to Harrison himself,Gen Z editors are the best-equipped to help Wikipedia survive and, possibly, even thrive in this new context: he pointed outa 2022 survey reporting that about 20% of Wikipedia editors were between the ages of 18 and 24, while also noting the role of young contributors inrecent debates on the incorporation ofchatbot-generated content on the encyclopedia. The article notably includes a short interview with a very prominent Gen Z editor: the latestWikimedian of the Year,Hannah Clover.

As for those "tired old practices", Harrison has his say about the sometimes inflexible norms and normalizing institutions of Wikipedia, not to mentionmobile-unfriendly editing interface, which he calls "issues that dissuade the younger generation from joining the cause". For instance, he says that the tasks taken on by new editors from a decade ago – ones letting them dip their toes in the editing experience in a low-risk, low-consequence environment – are now more highly automated, leaving a lack of "clear entry points". This in turn may lead today's new editors to unknowingly get intocontentious topics where they experience off-putting "harsh feedback" from the more established editors. Harrison left unsaid that there are more contentious topics and areas under sanctions than ever before (seepriorSignpost coverage that noted "policies of closure and the formalization of boundaries, rules and routines").

Whether the new generation can adapt to, or reform the tired Wiki, and eventually make it their own as they become the normies, or whether they abandon it for something new, only time can tell. –O,B

Joshua Yaffa inThe New Yorkerexplains(paywalled) the difficultiesMarlene Engelhorn had in giving away 25 million euros through theGuter Rat für Rückverteilung (Good Council for Redistribution). Engelhorn had inherited her money from a fortune that started with the founding ofBASF and later grew with theBoehringer Mannheim pharmaceutical company. She felt that she should give away most of it to reduce wealth inequality in Austria and as a learning experience to guide others who have the same goal. Engelhorn was keeping about 10% of her money and about €3 million was spent on implementing a process where acitizen council – a group of 50 ordinary Austrians selected by lottery – decided where the money should go. This included the use of moderators who "wield huge power" according to an academic who studies this area. They have "an emphasis on getting things done ... it can all mean that, in the moment, you take away the possibility for improvisation or dissent.”

Nearly eighty organizations were selected by the council, with an average of €312,500 for each organization. "Wikipedia" (as they called the Wikimedia Foundation) turned out to be the most controversial choice:

The idea came from a Vienna resident in his mid-thirties [...]. He saw Wikipedia as addressing many of the council’s core values: democracy, accessibility, transparency. The idea was immediately opposed by Kyrillos, a high-school student and the council’s youngest member. “We have a lot of other, more important issues to address here,” he said. Anyway, he went on, his teachers wouldn’t allow him to use Wikipedia as a source in his papers—why give it money?
Factions emerged. Some saw Wikipedia, a nonprofit based in the U.S., as an inefficient use of the council’s resources. Others viewed the effort to nix it as a violation of the council’s ground rules. [...]
The Wikipedia debate was ultimately settled with a compromise. The members of the education group agreed to give the organization fifty thousand euros, a small portion of their total.

Thanks Marlene!

The name "Engelhorn" may ring a bell for longtime Signpost readers. In 2015, theReiss Engelhorn Museum in Mannheim, Germany, filed lawsuits against the Wikimedia Foundation, Wikimedia Deutschland and a Wikimedia Commons user over the use of photographs of public domain artworks on Wikimedia projects. (Cf.Signpost coverage: "Wikimedia Foundation, Wikimedia Deutschland urge Reiss Engelhorn Museum to reconsider suit over public domain works of art", "Wikimedia lawsuits in France and Germany". While the museum prevailed in court against the Foundation, theEU Copyright Directive subsequently made such assertions of copyright over faithful reproductions of public domain works impossible.) Indeed the museum is so named after one of its sponsors, German industry titanCurt Engelhorn (1926–2016), a relative of Marlene Engelhorn. As detailed inthe German Wikipedia article about him, back in 1997, in what was Europe's largest company takeover to date, he had controversially managed to sell off the family's company holdings for 19 billionDM without paying any taxes to the German state, and Marlene Engelhorn has publicly criticized his (lack of) philanthropy.

–Sb,H

• "Can You Trust Dr. Wikipedia?": The Office for Science and Society ofMcGill University has recentlydiscussed the accuracy of Wikipedia's medical content and how difficult it is to address some questions. What's your purpose in studying "accuracy"? Which language version are you interested in? When was your sample of articles taken? Despite a horrifying lede invoking toasters (seepreviousSignpostcoverage) andJohn Seigenthaler, the authors conclude that it is "useful and fast" for many purposes, including for medical students approaching their licensing exams. They also praiseWikiProject Medicine and the use of classes that teach health science students to edit Wikipedia.
• New problems emerge in Iran: Pejman Amirireports inNewsBlaze about theIranian government's attempts to manipulate and censor theFarsi Wikipedia, while openly accusing the head of theIranian Wikimedians User Group, Mohammad Heydarzadeh (known on-Wiki asDarafsh), of "trying to gain higher and sensitive access to the Wikimedia Foundation", among other things. While much of this report is very difficult forThe Signpost to verify, you can seepreviousSignpost coverage of a global ban possibly linked to actions of the regime.
• Do it, or else: According toa recent report fromOC Media, Farid Pardashunas, an Azerbaijani blogger who had received apresidential award in 2021, revealed the identities of contributors to theAzerbaijani Wikipedia – including adminSolavirum – who had deleted about 3,000 articles on soldiers killed in theSecond Nagorno-Karabakh War; in what essentially representsblackmail, the blogger urged the contributors to restore the pages, while threatening to send their personal data to the nationalState Security Service.

• Bias in Judaism and Zionism related articles: Many newspapers have looked atpotential bias and reliability issues in articles relating toJudaism and/orZionism:
  • "Wikipedia defines Zionism as 'colonialism', sparking outrage" (Israel Hayom)
  • "War over Wikipedia's Definition of Zionism Pits Provoked Users Against Biased Editors" (The Jewish Press)
  • "Wikipedia blasted for 'wildly inaccurate' change to entry on Zionism: 'Downright antisemitic'" (Washington Examiner)
  • "From Bias to Balance: Jewish Editors on Wikipedia" (Times of Israel blogs)
  • "Wikipedia has an antisemitism problem" – opinion, (The Jerusalem Post)
  • "Wikipedia's anti-Israel bias is a feature, not a bug" – (The Jewish Journal of Greater Los Angeles)
  The latter source mentions there wasan attempt at the reliable sources noticeboard to downgradeThe Jewish Chronicle back in May. However, it should be noted that the aforementioned newspaper is just now in the middle ofa notable scandal involving fabricated news stories and opaque ownership, withIsraelisources first sounding the alarm. See also theongoing discussion over at the Noticeboard.
• Paid editing saga involving Portland commissioner Rene Gonzalez continues: Several regional media sources, includingThe Oregonian andKOIN, reported thatPortlandcity commissioner andcandidate for mayorRene Gonzalez has been cleared of wrongdoing by theCity Auditor's Office on September 16, as evidence about Gonzalez's use of local funds forpaid editing on Wikipedia wasdeemed as insufficient to prove a violation of the city'scampaign finance law. See previousSignpost coverage on the matterhere andhere.
• Do Wikipedia – responsibly: Whilst being best known as an actor andthe firstborn son ofBrad Hall andJulia Louis-Dreyfus, Henry Hall is also a musician. Back in August, hereleased his second studio album,Stop Doing Funny Stuff, which contains the single "Wikipedia-ing Poison Snakes", a solemn-sounding testimony from aterminally online man who keeps going down rabbit holes on Wikipedia, ranging frompredictive text toBooksmart, in order to distract himself from "his own head, his own bed, every word he's ever [freaking] said". If you feel like this song is too relatable, just know we're in this together (and many of us need some help).

In the wake of ahigh-profile sexual assault in Kolkata last month, India's courts have demanded that Wikipedia remove of the name of a victim from an article on the crime. While some national and local media outlets reported the name of the victim at the time, as did various international media sources, the laws of India, prohibit media from publicizing the names of victims of especially heinous crimes.

The incident was widely reported on 10 August, the day after it occurred; English Wikipedia editors created the article a day after that. By 13 August, editorsbegan debating whether to mention the victim's name. On 16 August, that debate became part of a discussion on how to title the article, which was ongoing until 9 September, when the disagreement about including the name split into its own discussion. All of this was part of theconsensus-building process usual and familiar to editors, where editorial decisions are reached by group discussion about how to implement policies and guidelines.

On 16 September, theSupreme Court of India ordered Wikipedia to remove the name.The Free Press Journal (report),The Hindu (report), andThe Times of India (report) are among the many sources to report on the court's order. In this case, and as often happens when institutions make requests of Wikipedia, the court made its request with some presumption that Wikipedia has an editorial leader who can issue binding orders. As is known among Wikipedia editors, there is no such person: even the Wikimedia Foundation does not control the content of articles under Wikipedia's policies.

In response to the court decision, the legal department of the Wikimedia Foundation posteda notice on the talk page of the article, encouraging Wikipedia editors todeliberate carefully on the issue and "explain clearly why you feel the balance of interests lies one way or the other, in order to reach consensus accordingly". Wikipedia editors did that, andreached a decision to exclude the victim's name.User:Tamzin, a volunteer editor, closed the discussion and authoredthe consensus statement.

The final decision was to exclude the name from the article. While closing statements typically are descriptions written by Wikipedia editors and for Wikipedia editors, Tamzin included a summary explanation of the overall process in anticipation that the court, media, and public observers may wish to examine both the consensus and discussion. It will not come as a surprise to Wikipedia editors that Wikipedians value their editorial independence. The closing statement emphasizes that Wikipedia editors did not arrive at that decision at the behest of the court, but rather because community deliberation found reasons for doing so and because a supermajority of editors supported the decision.

The article is2024 Kolkata rape and murder incident, andit begins, "On 9 August 2024, a 31-year-old female postgraduate trainee doctor at R. G. Kar Medical College and Hospital in Kolkata, West Bengal, India, was raped and murdered in a college building."

One set of arguments about the name relates to victims' rights and women's rights. The argument in favor of naming the victim is that her story becomes known and enables activism to reduceviolence against women. The argument opposed is that in some cases, and this case in particular, naming the victim greatly endangers and disturbs their family, social network, colleagues, and supporters.

Another set of arguments relates tocensorship of Wikipedia and Wikipedia's ownWP:NOTCENSORED policy. The argument in favor of publishing the name is that maximal freedom in publishing is the preferred position. The argument opposed is that naming the victim is not a censorship issue, as Wikipedia will definitely have an article on the crime, and that article does not benefit significantly by including the name of the victim.

Another set of arguments is about following the lead of what other media outlets do. Arguments in favor of publishing the name point to seemingWP:Reliable sources and reputable journalists who are publishing the name. Arguments opposed to publishing the name make various claims, including that sources publishing the name are mistaken, or that they have since removed the name, or that the higher quality sources do not publish the name while lower quality sources do. Wikipedia editorUser:Fowler&fowler checkedvarious sources and reported which ones do not publish the name.

A final set of arguments is on the practicality of collaboration between Wikipedia and the government of India. The argument in favor of publishing the name assumes that other arguments establish that Wikipedia editors should publish the name, and in that context, it is best for Wikipedia as an international media source outside the jurisdiction of Indian government control to disregard the government request. Arguments opposed to publishing the name include respect for the expertise of those courts, respect for national decision making to know what is best for local culture, anticipation of a good future of peaceful collaboration with the government of India by granting this request, and concern for the burden on Wikipedia editors in India if they bear the responsibility of an online global decision including non-Indian Wikipedia editors.

–BR

In 2011, the Australian Paralympic Committee (nowParalympics Australia) commenced a project to document its history. This included collecting documents and museum pieces and conducting oral history interviews with Paralympians. An online component was recognised as being important, and Wikipedia was identified as part of that. Since then, Paralympics Australia andWikimedia Australia have collaborated to produce thousands of articles that keep receiving millions of page-views each year. As part of the project, I attended theParalympic Games inLondon in 2012, inRio de Janeiro in 2016, and nowin Paris in 2024 as a media representative, with accreditation supplied by Paralympics Australia. This time, I took a photographer,GailLeenstra, with me.

• 
  Equestrian – France's Alexia Pittier
• 
  The most iconic of the Paralympic venues, the Equestrian on the grounds of theChateau de Versailles

Media accreditation meant that I had access to the media tribunes at the venues and could attend any game, even when the event was sold out (as was usually the case). It meant that I could visit theParalympic Village and interview athletes after the game in what is called the Mixed Zone. It meant that we could use the buses of theTC, the Olympic transport system. It meant we had access to the resources of the Main Press Centre (MPC) and the Venue Media Centres (VMCs), which providedwired andwireless internet access, desks to work at, staff to help us, and lockers to store our equipment. It meant that my photographer had access to prime photographic positions not accessible to the public. It also meant that she had access to theNikon store at theStade de France, where she was able to get some of her equipment repaired and borrow some very expensive equipment for the duration of the games to supplement the gear she had brought with her from Australia – all for free.

• 
  Opening ceremony – as seen from the media tribune
• 
  Opening ceremony – dancingPhryges

The image of Wikipedia has undergone a dramatic transformation in the time I have been working on the Australian Paralympic Project. In London in 2012, there was a tendency of the mainstream media to regard us as not being "real journalists". There was none of that in Paris, quite the opposite in fact; mainstream media representatives repeatedly told us how much they appreciated our efforts, how they used Wikipedia as a reference all the time, and how impressed they were with its accuracy.

• 
  Wheelchair basketball – Team USA – From left to right:Rose Hollermann,Courtney Ryan,Rebecca Murray,Natalie Schneider andLindsey Zurbrugg
• 
  Swimming – Australia'sTimothy Hodge

Support from Paralympics Australia did not end in Australia. In Paris, they had set up headquarters at a site near the Paralympic Village known as "Our Mob", which contained meeting rooms, a TV studio, dining room and aMcCafé concession (McDonald's being one of their sponsors). Tim Mannion, the General Manager of Communications, gave generous and welcome assistance and support to our efforts, including providing passes to theopening andclosing ceremonies. Unlike theOlympic opening ceremony, the Paralympic opening ceremony was held in beautiful weather. Some 65,000 spectators packed into thePlace de la Concorde for the first ever Paralympic opening ceremony to be held outside a stadium. GailLeenstra was one of a select group of photographers chosen to accompany the lighting of theParalympic cauldron.

• 
  Cycling – Australia'sJessica Gallagher andCaitlin Ward
• 
  Paracanoeing – From further to closer: Hungary's Dalma Boldizsar, Thailand's Wasana Khuthawisap, Australia'sSusan Seipel and Great Britain'sCharlotte Henshaw

InSydney, London and Rio, multiple venues were concentrated in a multi-sport precinct, but in Paris, the venues were widely scattered around the city. This is a model considered by many cities planning to hold the Olympics and Paralympics, because it allows the city to make use of existing facilities, saving the substantial cost of building new ones. It is not cheap, however! It came at a substantial cost in increased security, transportation and manpower through duplication. Venues required considerable upgrades, refurbishment and fitting out for the games. Three new venues had to be built, andParis Metro lines were extended. Not to mention the 1.6 billioneuros spent on cleaning up theSeine andMarne to make them fit to swim in. The police presence was overwhelming, with about 45,000 police and 10,000 troops on hand. All were heavily armed, with automatic weapons in caseHamas decided to put in an appearance. Roads near the venues were closed to vehicle traffic.

• 
  Wheelchair rugby – Australia'sChris Bond against Great Britain
• 
  Wheelchair rugby – Australia'sChris Bond against France

Getting from one venue to another involved a trip on the Paris Metro using theNavigo cards issued to us as part of our media kit. Each day we criss-crossed the city on the Metro as we moved from one venue to the next. Fortunately, the Metro was super-efficient, with trains leaving every couple of minutes. Getting to venues in themetropolitan area took about a half an hour. (The locals told us that the Metro had never been so efficient nor, with the enhanced police presence, had they ever felt safer.) This meant that each day started with critical decisions about what events we would cover that day. Priority was given to events with Australian participation (especially medal chances), since our media accreditation was so generously provided by Paralympics Australia, but the athletes of other countries (especially the English-speaking ones) were by no means neglected. A mobile phone app told us which trains and buses to take to get from one place to another. It knew the location of the venues, train stations and bus stops, the bus and train schedules, and how crowded they were, encouraging you to take less crowded services.

• 
  Wheelchair basketball – the Netherlands'Cher Korver,Saskia Pronk andYlonne Post, and Canada'sArinn Young andRosalie Lalonde
• 
  Wheelchair basketball – Canada'sKady Dandeneau,Rosalie Lalonde andPuisand Lai and Great Britain's Jade Atkin,Robyn Love andLaurie Williams

As it turned out, there were some other foreign Wikipedians present, but they lacked our accreditation and (quite understandably) had different priorities. This meant that Wikipedia had broad coverage and having Australian Wikipedians on site was fully justified by the coverage. We tried to see as many sports as possible: our coverage includedBoccia,Cycling,Equestrian,Paracanoe,Triathlon,Wheelchair Basketball andWheelchair Rugby. Cycling and Equestrian events were located well out of town, requiring a day trip on the Metro,Réseau Express Régional, and the TC.

• 
  Paralympics Australia PresidentAlison Creagh (left) and AustralianChef de MissionKate McLoughlin (centre) presentHawkeye7 with a scarf
• 
  Boccia – Australia'sJamieson Leeson and Hong Kong'sHo Yuen Kei

Between 23 August and 9 September, articles created by theHistory of the Paralympic movement in Australia project garnered a total amount of 2,226,684 page views, while more than 1,200 images were uploaded. These pages and pictures will be a lasting legacy, to be enjoyed by readers for years to come.

asilvering'srequest for adminship was closed as successful on September 6, 2024.

First, the bullet points:

• It turns out a week is a really, really long time;
• Even if your RfA isn't contentious or stressful, it will probably be exhausting;
• Goodnominators might not beessential, but I can't imagine having done RfA without mine; a steady hand on the tiller really, really helps (thanks especially toVanamonde93);
• Designated monitors are a blessing (thankstheleekycauldron), especially when you're doing the discussion-first format and you would like some acknowledgement that you didn't somehow transclude your RfA straight into the void;
• If I may offer advice to future RfA hopefuls: don't post answers to questions right away. Write the answers in a text file, go take a nap, andthen post them.
• If I may offer advice to future RfA participants: if you could not, without being overcome by embarrassment, apply your question/vote to both a) a teenager of your acquaintance and b) your grandmother, you should probably rephrase it.

As for how I got here:Vanamonde93 asked me if I was interested in running some time ago, but I deferred for a number of reasons that became more obviously hollow as time went on. For one, "I'm too busy" doesn't work as a very good excuse if you just go find something else to do, like starting a new kind ofGANbacklog drive. My second nominator was an easy choice, but that doesn't mean I wasn't nervous about asking. (Don't laugh,czar!) Reading the nomination statements made my heart grow three sizes and also made me want to disappear into the wallpaper. (I'm not good at praise.)

For a little while, I was of a mind to wait out the end of thediscussion-period trial, but once I'd got my nominators lined up, it started to feel like I just needed to get it over with. Then, right after I'd said, "alright, now's good, let's do this,"Femke showed up in my inbox asking if I'd ever thought of running. It felt so good to be able to tell someone, "you're just in time!" instead of brushing them off with some kind of excuse.

I tried my level best to ignore all the discussion and voting while it was happening, but it turns out that's really hard – not just because it takes willpower, but also becauseMediaWiki kept showing me the discussion section whenever I tried to preview my answers to the questions. And it's very hard to avoid knowing the precise count once voting starts, since that's right at the top of the page. So I gave up, and read everything. If you're thinking of RfA and you're the anxious sort who will be constantly fighting the temptation to check in, line up something to do with friends/family so you don't glue yourself to the refresh button. There's no way around this bit. It sucks, and a week is a long time.

Luckily for me, people had really nice things to say. I really appreciate everyone who participated and everyone who came by to congratulate me afterwards. I'm doing my best to avoid themushroom effect and to remainindifferent to both praise and blame. Thank you all for ignoring and then quietly removing the joke oppose; I was a bit worried they'd be murdered. As for the discussion period, I do think it helped things remain civil and on-track, but I'm ambivalent on the experiment overall.

Being an admin has been fun so far. Everyone's been really helpful, and if I've been driving anyone crazy with all my questions, they've been kind enough not to tell me. Special thanks toAoidh, who has already stopped me from doing something stupid, kindly and firmly, like disarming a wayward toddler running with scissors.

Maybe my opinion isn't worth much, since I had such a smooth RfA compared to so many others, but if you think you'd probably succeed at RfA and you're holding back since it sounds like a bad way to spend a week: I think you should go for it.

It's possible that someone will drag up something stupid you said four years ago and try to rub your nose in it, or that some personal flaw of yours will be magnified beyond all reason. It's possible some people will try to use your RfA as a soapbox to complain about policies or norms they dislike. It's very likely that you will spend the whole week waiting for the other shoe to drop, whether any of those things happen or not. Even an uncontentious RfA can be an exhausting and unpleasant experience – mine was all of those things.

But it's also very likely that, over the course of a week, somewhere between one and three hundred people will show up to say something nice about you. Many of those people will be folks whose opinion you really, truly value. Some of them will be people you can't remember ever hearing of before, but who nonetheless have something deeply gratifying to say. You should run.

Plus, now I can tell my colleagues that I "have tenure, on Wikipedia." I'm sure they'll all be very impressed.

Soni, the author of this story, wasactive in drafting the current reform proposals for theRequests for adminship process.

As part ofWP:RFA2024, multiple RfA reform attempts have completed trials or are currently under review: you can read previous coverage on the matter byThe Signpost in the16 May issue.

There has already been consensus toadd a reminder of RfA civility norms toWP:RFA, as well aslimit suffrage to only extended-confirmed voters andformally require all nominees to also be extended-confirmed. All of these proposals were implemented in the last few months.

The "discussion-only period" trial has come to an end this month, having converted five different RfAs (non SNOW-closed) to have "discussion only" for the first two days out of the seven-day period. After this initial trial,Phase II discussions are ongoing to determine if this proposal will become permanent.

As per the outcome of the relatedPhase II discussion, admins can now designate themselves as monitors for RfAs, subject to minimum expectations for their conduct during the whole process. The full list can be found atWP:MONITOR. This proposal is intended to improve enforcement of civility guidelines during RfAs.

Phase II for the administrator recall proposal has also recently finished, having waited for a closer for several months. It will allow a community-initiated path to de-adminship by requiring certain admins to submit and pass their RfA again.Further discussion is ongoing on the next steps for this process.

Finally, theAdmin Elections procedure is expected to trial in October: it will be a one-time trial to allow an alternate path to adminship, parallel to RfA. Candidates can sign up from 8 to 14 October, before entering a discussion period from 22 to 24 October, which will then be followed by aSecurePoll private voting session from 25 to 31 October. –S

Thespecial elections for theUniversal Code of Conduct Coordinating Committee (U4C) concluded earlier this month, with the election of just one candidate. With 613 votes cast between the 18 eligible candidates, onlyAjraddatz (for the North America seat) achieved the 60% support-to-support+oppose ratio required. This gives the U4C just enough members (8 out of 16 seats) to establish theirquorum, though it remains to be seen how U4C will handle inactive members.

The committee was set up primarily to deal with larger-scale disputes within smaller Wikis and to enforce theUniversal Code of Conduct across the various projects; they are expected to begin hearing cases shortly. Further information can be found on theU4C announcements page.

The full results of the U4C elections can beviewed here. This cycle had already been covered in the22 July issue ofThe Signpost. –S

The Wikimedia Foundation published their bulletins forlate August andearly September. Among other news, they covered apublic survey intended to better understandWikiProjects, the recentdisbandment of the MCDC and theWMF Board of Trustees election, which is currentlyin its scrutiny phase.

It was also mentioned that the WMF will briefly switch the traffic between its data centers for maintenance purposes on 25 September, starting at 15:00UTC. A banner will be displayed on all Wikis 30 minutes before the start of the operation, during which users will be able to read, but not edit, the sites for up to an hour. More information on the server switch can be foundhere.

Editors may also be interested in testing forthe Charts Extension and theAlt Text experiment on the iOS app, thecodified new API policy, or the WMF'snewest update on Movement Strategy Grants (Spoilers: it focuses on Hubs). –S,O

In response to the increased prevalence ofgenerative artificial intelligence, some editors of theEnglish Wikipedia have introduced measures to reduce its use within the encyclopedia. Using images generated fromtext-to-image models on articles is often discouraged, unless the context specifically relates to artificial intelligence. A hardlineLuddaite approach has not been adopted by all Wikipedians and AI-generated images are used in some articles in non-AI contexts.

Theimage guidelines generally restrict the use of images that are solely fordecorative purposes, as they do not contribute meaningful information or aid the reader in understanding the topic. Despite this restriction, it appears that paintings are permitted to be included in medical articles to display human-made artistic interpretations of medical themes. They offer historical and cultural perspectives related to medical topics.

• 
  The Nightmare, byHenry Fuseli onSleep paralysis
• 
  At Eternity's Gate, byVincent van Gogh onMajor depressive disorder
• 
  Narcissus, byCaravaggio onNarcissistic personality disorder
• 
  Venus with a Mirror, byTitian onBody image
• 
  The Scream, byEdvard Munch onAnxiety disorder. This painting is regarded as a symbol of the anxiety of the human condition.
• 
  The Imaginary Illness, byHonoré Daumier onHypochondriasis
• 
  Man with Delusions of Military Command, byThéodore Géricault onDelusional disorder
• 
  Le Suicidé, byÉdouard Manet onSuicide. This painting clearly depicts a suicide.
• 
  Melancholia, byTadeusz Pruszkowski onAnhedonia
• 
  The Sick Girl, byMichael Peter Ancher onDisease
• 
  Dance at Molenbeek, byPieter Brueghel the Younger onMass psychogenic illness

See also:Wikipedia:WikiProject AI Cleanup/AI images in non-AI contexts

WikiProject AI Cleanup searches for AI-generated images and evaluates their suitability for an article. If any images are deemed inappropriate, they may be removed to ensure that only relevant and suitable images are kept in articles.

• 
  Removed fromShrinkage (accounting) as a real-life alternative could be used instead.
• 
  Removed fromKemonā for being unnecessarily explicit.
• 
  Removed fromDarul Uloom Deoband for bad anatomy and risk of being mistaken for a contemporary work (the college was founded in 1866).
• 
  Removed fromThe Moon is made of green cheese because a historical illustration was already used in the article.

Perhaps the removed "scientific" images are the worst ones, however, even if they only affected one article,Chemotactic drug-targeting:

• 
  This is supposedly an amoeba moving; it looks more like a sperm cell, if anything.Amoeboid movement and aflagellum are fundamentally different movement techniques, and this looks much more like the latter. Prompt apparently was "an ameba moving toward a food source through the process ofchemotaxis". Chemotaxis is movement in response to chemicals in the surrounding environment andneeds a lot more creativity to illustrate.
• 
  This image inaccurately shows a red tumour on a cell. Cancer cells don't get cancerous tumours on them, they form tumours in aggregate. This is probably based on a cross between a tumour and images ofT lymphocytes attacking cancerous cells, but the combination created nonsense. The prompt was apparently a "cancer cell and its abnormal growth", but that's not meant to be growths on the cell itself.
• 
  Supposedlyleukocytes, but leukocytes aren't crystal orbs with vaguely red-blood cell shapes around and inside them. This one is more subtly wrong (it knows there should be something inside, and there's usually some red blood cells mixed in with them in photos...), which arguably makes it more insidious. This just isn't as blatantly bad, making it easier to mistake it as a real image.

It may also be worth considering what kind of AI artis being left in articles by the WikiProject:

• 
  Advertisement for "Willy's Chocolate Experience", a disastrous event in Glasgow, Scotland that used AI for all its promotional material. Without cropping out the fake words. Apasadise of sweet teats, indeed!
• 
  Succeeds in illustratingpastoral science fiction in a way that captures important ideas of sustainable structures.
• 
  These somewhat deformed buildings are apparently typical of ways the meme/hoax country ofListenbourg was depicted using... well, AI images.

Note: The following section is accurate as of the day before publication.

Policies vary between different language versions of Wikipedia. Differences in opinion among Wikipedians have resulted in the inclusion of text-to-image model-generated images on several Wikipedias, including the English Wikipedia. Many Wikipedias useWikidata to automatically display images, which takes place beyond the scope of local projects.

• 
  22nd century [ro] on the Romanian Wikipedia
• 
  Abchanchu [fr] on the French Wikipedia
• 
  Afanc [fr] on the French Wikipedia
• 
  Anachronism [no] on the Norwegian Wikipedia
• 
  Archimedes [ar] on the Arabic Wikipedia
• 
  Arcturians (New Age) on the English Wikipedia
• 
  Battle of Dhu al-Qassah [fr] on the French Wikipedia
• 
  Chuan Ralla [an] on the Aragonese Wikipedia
• 
  Cleopatra [cs] on the Czech Wikipedia
• 
  Climate fiction [fr][uk] on the French and Ukrainian Wikipedias
• 
  Colonization of Mars [fr] on the French Wikipedia
• 
  Cosso [gl] on the Galician Wikipedia
• 
  Cyberpunk [hu] on the Hungarian Wikipedia
• 
  Dagon [he] on the Hebrew Wikipedia
• 
  Danmei [fr] on the French Wikipedia
• 
  Darul Uloom Deoband [ks] on the Kashmiri Wikipedia
• 
  Dystopia [es] on the Spanish Wikipedia
• 
  Global catastrophic risk [ha] on the Hausa Wikipedia
• 
  Golem [es] on the Spanish Wikipedia
• 
  The Great God Pan [he] on the Hebrew Wikipedia
• 
  Gwyn ap Nudd [fr] on the French Wikipedia
• 
  Invisible religion [cs] on the Czech Wikipedia
• 
  Iratxoak [ca] on the Catalan Wikipedia
• 
  John F. Kennedy [rw] on the Kinyarwanda Wikipedia
• 
  LHS 1140 b [ru] on the Russian Wikipedia
• 
  Mikelats [ca][fr] on the Catalan and French Wikipedias
• 
  Mooring [sv] on the Swedish Wikipedia
• 
  Pastoral science fiction on the English Wikipedia
• 
  Preiddeu Annwfn [ru] on the Russian Wikipedia
• 
  Raposa do aire [gl] on the Galician Wikipedia
• 
  Religious delusion [nl] on the Dutch Wikipedia
• 
  Rendlesham Forest incident [es] on the Spanish Wikipedia
• 
  Science fantasy [eu][es] on the Basque and Spanish Wikipedias
• 
  Security hacker [ar][es][he][ks][ko] on the Arabic, Spanish, Hebrew, Kashmiri, and Korean Wikipedias
• 
  Silurian hypothesis [nl] on the Dutch Wikipedia
• 
  Silurian hypothesis [fr][es] on the French and Spanish Wikipedias
• 
  Space travel in science fiction, recently removed from the English Wikipedia
• 
  Sundiata Keita [es], recently removed from the Spanish Wikipedia
• 
  Technosignature [es] on the Spanish Wikipedia
• 
  Twin paradox on the English Wikipedia
• 
  Unicorn [pt] on the Portuguese Wikipedia
• 
  A Voyage to Arcturus [he] on the Hebrew Wikipedia
• 
  World Poetry Day [d] on many Wikipedias via Wikidata
• 
  Xan Quinto [ca] on the Catalan Wikipedia

A monthly overview of recent academic research about Wikipedia and other Wikimedia projects, also published as theWikimedia Research Newsletter.

A preprint titled "Language Agents Achieve Superhuman Synthesis of Scientific Knowledge"^[1] introduces

"PaperQA2, a frontier language model agent optimized for improved factuality, [which] matches or exceeds subject matter expert performance on three realistic [research] literature research tasks. PaperQA2 writes cited, Wikipedia-style summaries of scientific topics that are significantly more accurate than existing, human-written Wikipedia articles."

It was published by "FutureHouse", a San-Francisco-based nonprofit working on"Automating scientific discovery" (with a focus on biology). FutureHouse waslaunched last year with funding from former Google CEOEric Schmidt (at which time it was anticipated it would spend about $20 million by the end of 2024). Generating Wikipedia-like articles about science topics is only one of the applications of "PaperQA2, FutureHouse's scientific RAG [retrieval-augmented generation] system", which is designed to aid researchers. (For example, FutureHouse also recently launched a website called "Has Anyone", described as a "minimalist AI tool to search if anyone has ever researched a given topic.")

In more detail, the researchers "engineered a system called WikiCrow, which generates cited Wikipedia-style articles about human protein-coding genes by combining several PaperQA2 calls on topics such as the structure, function, interactions, and clinical significance of the gene." Each call contributes a section of the resulting article (somewhat similar to another recent system, see our review: "STORM: AI agents role-play as 'Wikipedia editors' and 'experts' to create Wikipedia-like articles"). Theprompts include the instruction to "Write in the style of a Wikipedia article, with concise sentences and coherent paragraphs".

With an average cost of $5.50, the generated articles tended to be longer than their Wikipedia counterparts and had higher quality, at least according to the paper's evaluation method:

We used WikiCrow to generate 240 articles on genes that already have non-stub Wikipedia articles to have matched comparisons. WikiCrow articles averaged 1219.0 ± 275.0 words (mean ± SD, N = 240), longer than the corresponding Wikipedia articles (889.6 ± 715.3 words). The average article was generated in 491.5 ± 324.0 seconds, and had an average cost of $4.48 ± $1.02 per article (including costs for search and LLM APIs). We compared WikiCrow and Wikipedia on 375 statements sampled from the 240 paired articles. [...] The initial article sampling excluded any Wikipedia articles that were "stubs" or incomplete articles. Statements were then shuffled and given, blinded, to human experts, who graded statements according to whether they were (1) cited and supported; (2) missing a citation; or (3) cited and unsupported. We found that WikiCrow had significantly fewer "cited and unsupported" statements than the paired Wikipedia articles (13.5% vs. 24.9%) (p = 0.0075,χ² (1), N = 375 for all tests in this section). WikiCrow failed to cite sources at a 3.9x lower rate than human written articles, as only 3.5% of WikiCrow statements were uncited, vs. 13.6% for Wikipedia (p < 0.001). In addition, defining precision for WikiCrow as the ratio of cited and supported statements over all cited statements, we found that WikiCrow displayed significantly higher precision than human-written articles (86.1% vs. 71.2%, p = 0.0013).

For the judgment whether a particular statement was "supported" by the cited references, the concrete question asked to the graders (described as "expert researchers" in the paper) was:

"Is the information correct, as cited? In other words, is the information stated in the sentence correct according to the literature that it cites?"

In addition, among other more detailed instructions, the graders were advised to mark a statement correct as cited even if it was not directly supported by the source, as long as the statement consisted of "broad context" judged to be "undergraduate biology student common knowledge" (akin to an extreme interpretation ofWP:BLUE).

The fact that these rating criteria appear to be more liberal thanWikipedia's own, combined with the well-known general reputation of LLMs for generatinghallucinations, makes the "WikiCrow displayed significantly higher precision" result rather remarkable. The authors double-checked it by examining the data more closely:

The "cited and unsupported" evaluation category includes both inaccurate statements (e.g. true hallucinations or reasoning errors) and statements that are accurate with inappropriate citations. To investigate the nature of the errors in Wikipedia and WikiCrow further, we manually inspected all reported errors and attempted to classify the issues as follows: reasoning issues, i.e. the written information contradicts, over-extrapolates, or is unsupported by any included citations; attribution issues, i.e. the information is likely supported by another included source, but either the statement does not include the correct citation locally or the source is too broad (e.g. a database portal link); or trivial statements, which are true passages, but overly pedantic or unnecessary [...]. Surprisingly, we found that compared to Wikipedia, WikiCrow had significantly fewer reasoning errors (12 vs. 26, p = 0.0144, χ2 (1), N = 375) but a similar number of attribution errors (10 vs. 16, p = 0.21), suggesting that the improved factuality of WikiCrow over Wikipedia was largely due to improvements in reasoning.

The authors caution that this result about Wikipedians "hallucinating"more frequently than AI is specific to their "WikiCrow" system (and the task of writing articles about genes), and must not be generalized to LLMs in general:

Although language models are clearly prone to reasoning errors (or hallucinations), in our task at least they appear to be less prone to such errors than Wikipedia authors or editors. This statement is specific to the agentic RAG setting presented here: language models likeGPT-4 on their own, if asked to generate Wikipedia articles, would still be expected to hallucinate at high rates.

A previous, less capable version of the WikiCrow system had already been described in aDecember 2023 blog post, which discussed the motivation for focusing on the task of writing Wikipedia-like articles about genes in more detail. Rather than seeing it as an arbitrary benchmark demo for their LLM agent system (back then in its earlier version, PaperQA), the authors described it as being motivated by longstanding shortcomings of Wikipedia's gene coverage that are seriously hampering the work of researchers who have come to rely on Wikipedia:

If you've spent time in molecular biology, you have probably encountered the "alphabet soup" problem of genomics. Experiments in genomics uncover lists of genes implicated in a biological process, likeMGAT5B andADGRA3. Researchers turn to tools like Google,Uniprot or Wikipedia to learn more, as the knowledge of 20,000 human genes is too broad for any single human to understand. However, according to our count, only 3,639 of the 19,255 human protein-coding genes recognized by theHGNC have high-quality (non-stub) summaries on [English] Wikipedia; the other 15,616 lack pages or are incomplete stubs. Often, plenty is known about the gene, but no one has taken the time to write up a summary. This is part of a much broader problem today: scientific knowledge is hard to access, and often locked up in impenetrable technical reports. To find out about genes like MGAT5B and ADGRA3, you'd end up sinking hours into reading the primary literature.

[The 2023 version of] WikiCrow is a first step towards automated synthesis of human scientific knowledge. As a first demo, we used WikiCrow to generate drafts of Wikipedia-style articles for all 15,616 of the Human protein-coding genes that currently lack articles or have stubs, using information from full-text articles that we have access to through our academic affiliations. We estimate that this task would have taken an expert human ~60,000 hours total (6.8 working years). By contrast, WikiCrow wrote all 15,616 articles in a few days (about 8 minutes per article, with 50 instances running in parallel), drawing on 14,819,358 pages from 871,000 scientific papers that it identified as relevant in the literature.

These challenges of covering the large number of relevant genes are not news to Wikipedians working in this area. Back in 2011, several papers in a special issue ofNucleic Acids Research on databases had exploredWikipedia as a database for structured biological data, e.g. asking "how to get scientists en masse to edit articles" in this area, and presenting English Wikipedia's "Gene Wiki" taskforce (which iscurrently inactive). In a 2020 article ineLife, a group of 30 researchers and Wikidata contributors similarly "describe[d] the breadth and depth of the biomedical knowledge contained within Wikidata," including its coverage of genes in general ("Wikidata contains items for over 1.1 million genes and 940 thousand proteins from 201 unique taxa") and human genetic variants ("Wikidata currently contains 1502 items corresponding to human genetic variants, focused on those with a clear clinical or therapeutic relevance").^[2] But it seems that at least from the point of view of the FutureHouse researchers, Wikidata's gene coverage is not a substitute for Wikipedia's, perhaps because it does not offer the same kind of factual coverage (see also the review of a related dissertation below).

The current paper is not peer-reviewed, but conveys credibility by disclosing ample detail about the methodology for building and evaluating the PaperQA2 and WikiCrow systems (also in an accompanyingtechnical blog post), and by releasing the underlying source code and data. The PaperQA2 system is available as anopen-source software package. (This includes a "Setting to emulate the Wikipedia article writing used in our WikiCrow publication". However, the paper cautions that the released version does not include some additional tools that were used, and in particular does not provide "access to non-local full-text literature searches", which are "often bound by licensing agreements".) The generated articles are available onlinein rendered form and asMarkdown source (see full list below, with links to their Wikipedia counterparts for comparison). Theannotated expert ratings have been published as well.

The authors acknowledge "previous work on unconstraineddocument summarization, where the document must be found and then summarized, and even writing Wikipedia-style articles with RAG" (i.e. the aforementioned STORM project). But they highlight that

"These studies have not compared directly against Wikipedia with human evaluation. Instead, they used either LLMs to judge or [like STORM] comparedROGUE (text overlap) against ground-truth summaries. Here, we measure directly against human-generated Wikipedia with subject [matter] expert grading."

The "crow" moniker (already used in a predecessor project called "ChemCrow",^{[supp 1]} an LLM agent working on chemistry tasks) isinspired by the fact that "Crows can talk – like aparrot – but their intelligence lies intool use."

From the abstract of a dissertation titled "Exploiting semi-structured information in Wikipedia for knowledge graph construction":^[3]

"[...] we address three main challenges in the field of automated knowledge graph construction using semi-structured data in Wikipedia as a data source. To create an ontology with expressive and fine-grained types, we present an approach that extracts a large-scale general-purpose taxonomy from categories and list pages in Wikipedia. We enhance the taxonomy's classes with axioms explicating their semantics. To increase the coverage of long-tail entities in knowledge graphs, we describe a pipeline of approaches that identify entity mentions in Wikipedia listings, integrate them into an existing knowledge graph, and enrich them with additional facts derived from the extraction context. As a result of applying the above approaches to semi-structured data in Wikipedia, we present the knowledge graph CaLiGraph. The graph describes more than 13 million entities with an ontology containing almost 1.3 million classes. To judge the value of CaLiGraph for practical tasks, we introduce a framework that compares knowledge graphs based on their performance on downstream tasks. We find CaLiGraph to be a valuable addition to the field of publicly available general-purpose knowledge graphs."

Why would one want to use Wikipedia as a source of structured data and build a new knowledge graph when Wikidata already exists? First, the thesis argues that Wikidata — even though it has surpassed other public knowledge graphs in the number of entitities — is still very incomplete, especially when it comes to information about long-tail topics:

"The trend of entities added to publicly available KGs in recent years indicates they are far from complete. The number of entities in Wikidata [195], for example, grew by 26% in the time from October 2020 (85M) to October 2023 (107M) [206]. Wikidata describes the largest number of entities and comprises – in terms of entities – other public KGs to a large extent [66]. Consequently, this challenge of incompleteness applies to all public KGs, particularly when it comes to less popular entities [44]. [...]

On the other hand, an automated process for extracting structured information from Wikipedia may not yet be reliable enough to import the result directly without manual review:

While the performance ofOpen Information Extraction (OIE) systems (i.e., systems that extract information from general web text) has improved in recent years [159, 97, 112], the quality of extracted information has not yet reached a level where integration into public KGs like Wikidata or DBpedia [104] should be done without further filtering. [...]
[...] first "picking low-hanging fruit" by focusing on premium sources like Wikipedia to build a high-quality KG is crucial as it can serve as a solid foundation for approaches that target more challenging data sources. The extracted information may then be used as an additional anchor to make sense of less structured data.

Chapter 3 ("Knowledge Graphs on the Web") contains detailed comparisons of Wikidata with other public knowledge graphs, with observations including the following:

The main focus of DBpedia is on persons (and their careers), as well as places, works, and species. Wikidata also strongly focuses on works (mainly due to the import of entire bibliographic datasets), while Cyc, BabelNet and NELL show a more diverse distribution. [...]

[...] Wikidata has the largest number of instances and the largest detail level in most classes. However, there are differences from class to class. While Wikidata contains a large number of works, YAGO is a good source of events. NELL often has fewer instances, but a larger level of detail, which can be explained by its focus on more prominent instances.
Wikidata contains about twice as many persons as DBpedia and YAGO [..., which] contain almost no persons which are not contained in Wikidata. In conclusion, combining Wikidata with DBpedia or YAGO for better coverage of the Person class would not be beneficial"

(see also an earlier paper co-authored by the author that was titled"Knowledge Graphs on the Web -- an Overview")

• See thepage of the monthlyWikimedia Research Showcase for videos and slides of past presentations.

Other recent publications that could not be covered in time for this issue include the items listed below. Contributions, whether reviewing or summarizing newly published research,are always welcome.

A Wikidata taxonomy (from"city or town" to"entity") before and after refinement

From the abstract:^[4]

"Wikidata is known to have a complex taxonomy, with recurrent issues like the ambiguity between instances and classes, the inaccuracy of some taxonomic paths, the presence of cycles, and the high level of redundancy across classes. Manual efforts to clean up this taxonomy are time-consuming and prone to errors or subjective decisions. We present WiKC, a new version of Wikidata taxonomy cleaned automatically using a combination of Large Language Models (LLMs) and graph mining techniques."

From the "Evaluation" section:

"As expected, WiKC is much simpler and much more concise than Wikidata taxonomy. Compared to WiKC, Wikidata taxonomy has a factor higher than 200 in the number of classes, and a factor higher than 10 in the average number of paths from an instance to the root class entity (Q35120)."
"WiKC consistently outperforms Wikidata across all depth ranges. WiKC shows significant accuracy gains at deeper levels (depth 10 or more), suggesting that WiKC has resolved many inconsistency issues in the lower levels of the Wikidata taxonomy."

From the abstract:^[5]

"Hundreds of thousands of articles on English Wikipedia have zero or limited meaningful structure on Wikidata. Much work has been done in the literature to partially or fully automate the process of completing knowledge graphs, but little of it has been practically applied to Wikidata. This paper presents two interconnected practical approaches to speeding up the Wikidata completion task. The first isWwwyzzerdd, a browser extension that allows users to quickly import statements from Wikipedia to Wikidata. Wwwyzzerdd has been used to make over 100 thousand edits to Wikidata. The second isPsychiq, a new model for predicting instance and subclass statements based on English Wikipedia articles. [...] One initial use is integrating the Psychiq model into the Wwwyzzerdd browser extension."

From the paper:^[6]

"Translations help people understand content written in another language. However, even correct literal translations do not fulfill that goal when people lack the necessary background to understand them. Professional translators incorporateexplicitations to explain the missing context by considering cultural differencesbetween source and target audiences. [...] For example, the name "Dominique de Villepin" may bewell known in French community while totally unknown to English speakers in which case the translator may detect this gap of background knowledge between two sides and translate it as "the former French Prime Minister Dominique de Villepin" instead of just "Dominique de Villepin". [...]
This work introduces techniques for automatically generating explicitations, motivated by WIKIEXPL, a dataset that we collect from Wikipedia and annotate with human translators. [...]

Our generation is grounded in Wikidata and Wikipedia—rather than free-form text generation—to prevent hallucinations and to control length or the type of explanation. For SHORT explicitations, we fetch a word from instance of or country of from Wikidata [...]. For MID, we fetch adescription of the entity from Wikidata [...]. For LONG type, we fetch three sentences from the first paragraph of Wikipedia."

From the abstract:^[7]

"Knowledge Graph Construction (KGC) can be seen as an iterative process starting from a high quality nucleus that is refined by knowledge extraction approaches in a virtuous loop. Such a nucleus can be obtained from knowledge existing in an open KG like Wikidata. However, due to the size of such generic KGs, integrating them as a whole may entail irrelevant content and scalability issues. We propose an analogy-based approach that starts from seed entities of interest in a generic KG, and keeps or prunes their neighboring entities. We evaluate our approach on Wikidata through two manually labeled datasets that contain either domain-homogeneous or -heterogeneous seed entities."

From the abstract:^[8]

"By analyzingthe edit history of Wikipedia's 'hyperpop' page, we locate ongoing debates, controversies, and contestations that point to shaping forces around online genre formation. These potentially have a huge impact on how hyperpop is understood both inside and outside of the music community. In locating the most active editors of the hyperpop Wikipedia page and scrutinizing their edit histories as well as thediscussions on the hyperpop page itself, we uncovered debates about artistic notability, biases toward specific sources, and attempts at associating or dissociating musical genre from non-musical identities (such as race, gender, and nationality)."

From the abstract:^[9]

"Paradoxically, in each language [English/French/Portuguese Wikipedia], the airplane has a different inventor. Through online ethnography, this article explores the multilingual landscape of Wikipedia, looking not only at languages, but also at language varieties, and unpacking the intricate connections between language, country, and nationality in grassroots knowledge production online."

Supplementary references:

This traffic report is adapted from theTop 25 Report, prepared with commentary byIgordebraga,Vestrian24Bio (August 25 to September 14),Ltbdl, andRahcmander (August 25 to 31),Oltrepier (September 1 to 7),Hubert555 (September 8 to 14).

Rank	Article	Class	Views	Image	Notes/about
1	James Earl Jones		3,034,998		One of the most respected African American actors ever, particularly for that booming voice that spoke iconic lines like "No. I am your father", "Everything the light touches is our kingdom", "Steel isn't strong, boy, flesh is stronger!" and "This isCNN", James Earl Jones had a storied career of nearly seven decades, resulting in him being one of the few who won theEGOT (albeit with an honorary Oscar). Retired ever since starring inComing 2 America in 2021, he died on September 9, at the age of 93.
2	Laura Loomer		1,434,117		Donald Trump's supporters are concerned about all the influence he's receiving from this activist and self-proclaimed "pro-white nationalist" and a "proudIslamophobe".
3	Kamala Harris		1,284,808		The vice-president and the Democrats' candidate in theupcoming election, who was on millions of screens atlast week's presidential debate.
4	The Greatest of All Time		1,153,526		ThisKollywoodscience fiction action film, starringThalapathy in his68th film in a lead role, was released last week. Earlier this year, the actorannounced his political entry, making this movie his penultimate work ever, thus his fans celebrated it worldwide. The film debuted at the second place at the worldwide box office, behind only #7, on its opening weekend. It also became the second Tamil film to enter the global weekend chart ofComscore, with the other film beingthe same actor'sprevious film. The film grossed₹350 crore (US$41 million) worldwide in its opening week, against a budget of₹300−400 crore, becoming the thehighest-grossing Tamil film of 2024 and thefourth highest-grossing Indian film of 2024. Thalapathy'snext and last film has now been announced, andthe fans are already preparing for the One Last Dance!
5	September 11 attacks		1,132,328		23 years have passed since this tragedy took place inthree differentUSlocations, where many lost their loved ones due to aterrorist attack masterminded byAl-Qaeda. For those interested, it also marks 23 years since the release of thecriminally underrated soundtrack from thefamously bad movie by this writer's favouritechanteuse,Mariah Carey. The picture on the left summarizes all of it.
6	The Perfect Couple (TV series)		1,010,108		This Americanmysterydrama series, which is an adaptation of the 2018 novel of the same name byElin Hilderbrand and includesNicole Kidman (pictured) in its cast, premiered onNetflix last week.
7	Beetlejuice Beetlejuice		1,009,928		36 years later, directorTim Burton and actorsMichael Keaton,Winona Ryder andCatherine O'Hara returned to a smallConnecticut city haunted by weird ghosts. Filled with Burton's signature aesthetics and many funny moments, including a musical number featuringone of the corniest songs ever written, this movie was warmly received and had an impressive $111 million opening in North America alone, which was already enough to cover its production budget.
8	Deaths in 2024		999,325		Given one of the quotes from #1 (better use this song now than whenthe unnecessary prequel arrives): 'Til we find our place On the path unwinding In the circle Thecircle of life
9	Beetlejuice		881,184		The death list interrupted, but with three mentions of his name, "it's showtime"! Fresh offPee-wee's Big Adventure, in 1988 Tim Burton directed the story of a recently deceased couple trying to scare away two yuppies (and their goth daughter) from their old house, at a certain point bringing inthe hyperactive undead maniac the movie is named after.The Geffen Company tried to get a sequel shortly after the movie, with Burton himself thinking about doubling the insanity inBeetlejuice Goes Hawaiian, but Betelgeuse and Lydia Deetz only hada cartoon anda musical adaptation before the proper follow-up listed above.
10	Rebel Ridge		679,584		This film, starringAaron Pierre as a formerMarine andDon Johnson as the chief of a corrupt police force, was released byNetflix on September 6 to positive reviews.

• These lists exclude the Wikipedia main page, non-article pages (such asredlinks), and anomalous entries (such as DDoS attacks or likely automated views). Since mobile view data became available to the Report in October 2014, we exclude articles that have almost no mobile views (5–6% or less) or almost all mobile views (94–95% or more) because they are very likely to be automated views based on our experience and research of the issue. Please feel free to discuss any removal on theTop 25 Report talk page if you wish.

For the August 16 – September 16 period, perthis database report.