Mediaboss Marketing

In the APSA Public Scholarship Program, graduate students in political science produce summaries of new research in the American Political Science Review. This piece, written by Syeda ShahBano Ijaz, covers the new article by Sverrir Steinsson, George Washington University, Rule Ambiguity, Institutional Clashes, and Population Loss: How Wikipedia Became the Last Good Place on the Internet.

If you have ever used Wikipedia, you might have noticed that even though the online encyclopedia is an open source that anyone can edit (even you!), it is able to maintain neutrality on most issues and is even open to labeling certain issues as false or a pseudoscience. But Wikipedia was not always this reliable; in his recent APSR article, Sverrir Steinsson investigates the evolution of English-language Wikipedia to find out how understanding of neutrality by Wikipedians evolved over time, ending up with increasing reliability of Wikipedia as a source to use. Steinsson traces the change in the content of English Wikipedia over time to suggest that the combination of ambiguous institutional rules and certain editors leaving the site helped Wikipedia transition from being a source that hosted pro-fringe discourse to one that gained credibility as an active fact-checker and anti-fringe. A close examination of the content of selected Wikipedia articles, their publicly available editing history, as well as the comments made by the editors, allows Steinsson to show that a change in the interpretation of Wikipedias Neutral Point of View (NPOV) guideline affected the nature of content in its articles. As the interpretation favored by anti-fringe editors became popular, pro-fringe editors faced increasing challenges and began to leave Wikipedia. This shift in the balance between pro-fringe and anti-fringe editors, which was a result both of the way editorial disputes were resolved and the exit of pro-fringe editors, made Wikipedia gain credibility as a source that debunked myths and controversies and did not promote pseudoscience.

Most institutional theorists consider institutions to be stable and biased toward the status quo. Institutions persist and tend to behave in the same way over time due to continuity in decision-making and membership stability. However, comparative politics literature on norm contestation suggests that reinterpretation of ambiguous norms can lead to institutions changing from within. The ambiguity in Wikipedias NPOV guideline provided the same opportunity for internal change. However, for such an internal change to occur, it is important that camps with coherent views exist and that contestation between these camps leads to clear victories. This leads to power shifts within the camps and allows manifest institutional change to occur.

Steinsson selects 63 Wikipedia articles that reflect diverse topic areas (such as climate, health, gender, sexuality, and so on) with issues that have been linked to controversies that favor a pro-fringe rhetoric. He analyzes these articles for changes over time to establish the presence of an internal institutional shift. The language of each article is coded on a five-point scale, ranging from fringe normalization to pro-active fringe busting. In addition to this content analysis, Steinsson also closely studies changes in Wikipedias governance structure. He finds that content in English Wikipedia changed over time, from being supportive of pseudoscience and conspiracy theories to active myth-busting. Take the example of the Wikipedia page on homeopathy: from 2001-2006, the lead on the page described homeopathy as a controversial system of alternative medicine. From 2006-2013, the content changed to mentioning that homeopathy has been regarded as pseudoscience and sharing that there is a lack of convincing scientific evidence confirming its efficacy. By 2015, this description had stabilized to homeopathy is a pseudoscience.

()the credibility gain of Wikipedia is an important case study that shows how internal reinterpretation of institutional norms can drive change. Steinsson suggests that the shift in language occurred because of an internal change in how Wikipedia editors interpreted the NPOV guidelines. From an early understanding of the NPOV rule as entailing diverse points of views and staying away from pejorative labels, the later understanding moved towards only documenting facts (as opposed to points of view) and the acceptance to apply pejorative labels as needed. Accompanying this change in understanding was an editorial powershift; Steinsson documents the editorial debates over time to show that anti-fringe editors gained ground while pro-fringe editors began to exit Wikipedia. As a result of this attrition, the institutional brand of English Wikipedia moved from being a suspect source to a credible one.

These changes in Wikipedias content have been gradual as opposed to sudden. Therefore, it is unlikely that they were prompted by external events like the election of Donald Trump in 2016. These shifts are also unlikely to reflect external shifts in the sources Wikipedia drew from since it was the analysis of the sources within Wikipedia articles that changed. Further, many of the sources the articles would cite as reliable were deemed unreliable over time. Instead, the credibility gain of Wikipedia is an important case study that shows how internal reinterpretation of institutional norms can drive change. This casts doubt on the stability of institutions, particularly those that encourage public engagement through social media and the internet.

See the rest here:
How Conflicts and Population Loss Led to the Rise of English ... - Political Science Now

The research article (preprint) below was recently shared on arXiv.

Title

Wikipedia And Open Access

Authors

Puyu Yang The University of Amsterdam

Ahad Shoaib cole Polytechnique Fdrale de Lausanne (EPFL) University of Waterloo

Robert West cole Polytechnique Fdrale de Lausanne (EPFL)

Giovanni Colavizza The University of Amsterdam

Source

via arXiv

DOI: 10.48550/arXiv.2305.13945

Abstract

Wikipedia is a well-known platform for disseminating knowledge, and scientific sources, such as journal articles, play a critical role in supporting its mission. The open access movement aims to make scientific knowledge openly available, and we might intuitively expect open access to help further Wikipedias mission. However, the extent of this relationship remains largely unknown. To fill this gap, we analyze a large dataset of citations from Wikipedia and model the role of open access in Wikipedias citation patterns. We find that open-access articles are extensively and increasingly more cited in Wikipedia. What is more, they show a 15% higher likelihood of being cited in Wikipedia when compared to closed-access articles, after controlling for confounding factors. This open-access citation effect is particularly strong for articles with low citation counts, including recently published ones. Our results show that open access plays a key role in the dissemination of scientific knowledge, including by providing Wikipedia editors timely access to novel results. These findings have important implications for researchers, policymakers, and practitioners in the field of information science and technology.

Direct to Full Text Article 16 pages; PDF.

Filed under: Data Files, Journal Articles, News, Open Access

Gary Price (gprice@gmail.com) is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. He earned his MLIS degree from Wayne State University in Detroit. Price has won several awards including the SLA Innovations in Technology Award and Alumnus of the Year from the Wayne St. University Library and Information Science Program. From 2006-2009 he was Director of Online Information Services at Ask.com. Gary is also the co-founder of infoDJ an innovation research consultancy supporting corporate product and business model teams with just-in-time fact and insight finding.

See more here:
Research Article: Wikipedia and Open Access (preprint) - LJ INFOdocket

We are back with the shocking news that a name is gaining the attention of the people. Tragic circumstances unfolded at the time of a birthday celebration at a St. Louis apartment, as the police reported that one cousin accidentally shot and killed the other before turning the firearm on herself. This news is getting circulated on the web and gaining the attention of the people. People are hitting the search engine to gain all the details about the news. What happened? What is the entire matter? We will try to cover all the details of the news. Lets continue the article.

According to the report, a tragic incident unfolded in Missouri where one young cousin brutally shot the other before taking her own life, as confirmed by the police and a relative. The individuals involved were recognized as Paris Harvey and her 14-year-old cousin Kuaron Harvey. They both live in St. Louis. The police department of St. Louis get a report about the incident shooting and the time was happening 2 am on Friday in the 1000 block of Spruce Street. Several things remain to tell you about the news, which you will find in the next section of the article.

On the basis of the report, The St. Louis Police Department has tried its best to find all the details about the news. They have tried their all resources to get the right information about the matter. They are handling the case in their own way. All the people are sharing their thoughts with the family of Paris Harvey and Kuaron Harvey, who left this world in this tragic incident, which took place on 25 March 2022. Yes, this incident happened around 1 year ago but still, this case is making headlines. Scroll down the page to know more information about the news.

Furthermore, the cousins who lost their lives were fatally shot while live-streaming on Instagram from the apartment bathroom at the time of a family party celebrating the birthdays of the younger one, who came into the world in March. Although the authorities are investigating the incident, initial reports give the indication it is being treated as a murder-suicide. We have shared all the details about the news, which we have fetched from other sources. All the possible news are included in this article. If we get any further detail we will tell you first at the same site. Stay tuned for more updates.

Read the original here:
Who Was Kuaron Harvey Cousin Paris Harvey? Wikipedia And Age - PKBnews.in

Members of the ultraconservative House Freedom Caucus are unhappy that the debt ceiling deal wouldnt significantly reduce federal budget deficits in coming years. One referred to the deal as a sandwich made of excrement, another called it insanity, and a third tweeted a barfing emoji. Classy stuff.

Realistically, though, there are two problems with the right wings position on deficits. One is that the rapid reduction in deficits those legislators call for would not be healthy for the economy, especially right now. The other is that while deficit reduction is important in the long term, right-wing Republicans are looking for balance in the wrong places.

On the first point, its lucky for the U.S. economy that the deal reached by President Biden, House Speaker Kevin McCarthy and their lieutenants is less aggressive than the House-passed Limit, Save, Grow Act of 2023, which the Congressional Budget Office estimated would reduce federal deficits by $4.8 trillion over 10 years.

Too much fiscal austerity too fast can harm the economy because the federal government takes money out of Americans pockets when it spends less (or taxes more). While the economy is running hot now, with unemployment in April matching the lowest since 1969, there are abundant signs that a recession is near. The Conference Boards index of leading economic indicators declined in April for the 13th consecutive month, signaling a worsening economic outlook, the board, a business-supported research group, announced.

Even the cuts in the debt ceiling deal would be a mild retardant for economic growth. As reported by The Times, the deal would hold nondefense spending in 2024 at roughly its 2023 level and would increase it by 1 percent in 2025. An initial estimate by The Times predicts that the limits would reduce federal spending by about $650 billion over 10 years, assuming that spending grows at the anticipated rate of inflation after the caps lift in two years.

Economically speaking, reducing federal budget deficits is important but not urgent. By the International Monetary Funds calculations, Japans central government debt totaled 221 percent of its G.D.P. in 2021, compared with 115 percent for the United States, and Japan seems to be doing OK. (Those numbers are somewhat exaggerated because they include debt held by other parts of the government, not just debt held by the public.)

Eventually, though, something will have to be done. In February the nonpartisan Congressional Budget Office projected that by current law, U.S. debt held by the public (a narrower measure than the I.M.F.s) will reach 195 percent of G.D.P. in 2053, double the level of 98 percent in 2023. At that point, an uncomfortably large portion of federal spending has to be devoted to paying interest on the debt. Theres no risk of default, because the government can always print more dollars to cover its debts, but too much money printing would make it hard to keep inflation under control.

That brings up the second thing thats wrong with the right wings condemnation of the debt ceiling deal. Freedom Caucus members, along with other Republicans and a fair number of Democrats, have unwisely ruled out tax increases as a key component of fixing the governments finances.

The drama around the debt ceiling deal, which is far from over, is intense because negotiators are trying to achieve something that is impossible. They are looking for all of their deficit reduction on the spending side, rather than a more reasonable mixture of spending cuts and tax increases.

Cutting Social Security and Medicare is tough because they are justly popular programs. They are lifelines for a large share of the public. They are growing because society is aging, not because older Americans are getting sweetheart treatment. Cutting defense is tough because the world is a perilous place (although I do think theres some fat to be pared). And cutting discretionary spending other than defense is tough because it accounts for only about 15 percent of outlays and does many valuable things, from funding scientific research to helping the poor to guaranteeing food safety. It would take devastating reductions in key functions of government to make a significant difference in the outlook for deficits and debts.

That leaves higher taxes as the underexplored option. According to the Congressional Budget Office, by current law, total outlays by the federal government are projected to rise to 30.2 percent of G.D.P. by 2053 from 23.7 percent in 2023. That big increase in outlays is not matched by a corresponding increase in revenue, which the C.B.O. projects will edge up to 19.1 percent in 2053 from 18.3 percent in 2023.

To keep debt from soaring, one of two things needs to happen. Either outlays need to increase more slowly as a share of G.D.P. or revenues need to increase more rapidly. I think the revenue option is going to come to the fore eventually.

Germanys economy is likely to shrink about 0.3 percent this year from 2022, a team of Deutsche Bank economists led by Stefan Schneider, the chief economist, wrote on Friday. Next year doesnt look great, either, the team said. With the expected U.S. recession weighing on German economic momentum toward year end and in early 2024, we have cut our annual forecast for G.D.P. growth in 2024 to 0.5 percent from 1 percent.

But one cant understand my ideas about Wikipedia without understanding Hayek.

Jimmy Wales, a founder of Wikipedia, blog post, July 17, 2005

Excerpt from:
Opinion | The Impossible Math of the Debt Deal's Detractors - The New York Times

from the questions,-questions dept

There have been various chapters in the new large language models (LLMs) story. First, people were amazed that systems like ChatGPT could write a sonnet about bananas in the style of Shakespeare, and in just a few seconds. Soon, though, they realized that chatbots replies might be grammatically correct, but they were frequently peppered with false information that the system simply made up, often with equally fake references. Were now at the stage where many are starting to think through the deeper implications of using LLMs, with all their powers and flaws, and how they will affect current working (and living) practices. As a post on the Vice site explains, one group grappling with this issue is the Wikipedia community:

During a recent community call, it became apparent that there is a community split over whether or not to use large language models to generate content. While some people expressed that tools like Open AIs ChatGPT could help with generating and summarizing articles, others remained wary.

Wikipedia already has a draft policy on how LLMs can be used when writing Wikipedia entries. The draft provides an excellent summary of some of the key problems of using chatbots, many of which will be faced by people in other domains. Here are the main points from the basic guidance section:

Do not publish content on Wikipedia obtained by asking LLMs to write original content or generate references. Even if such content has been heavily edited, seek other alternatives that dont use machine-generated content.

You may use LLMs as a writing advisor, i.e. asking for outlines, asking how to improve paragraphs, asking for criticism of text, etc. However, you should be aware that the information they give to you can be unreliable and flat out wrong. Use due diligence and common sense when choosing whether to incorporate the LLMs suggestions or not.

You may use LLMs for copyediting, summarization, and paraphrasing, but note that they may not properly detect grammatical errors or keep key information intact. Use due diligence and heavily edit the response. Dont hesitate to ask the LLM to correct deficiencies such as missing information in a summary or an unencyclopedic, e.g. promotional tone.

You are responsible for making sure that using an LLM will not be disruptive to Wikipedia.

You must denote that a LLM was used in the edit summary.

LLM-created works are not reliable sources. Unless their outputs were published by reliable outlets with rigorous oversight, they should not be cited in our articles.

It would be foolish to try to forbid Wikipedia contributors from using chatbots to help write articles: people would use them anyway, but would try to hide the fact. A ban would also be counterproductive. LLMs are simply tools, just like computers, and the real issue is not whether to use them, but how to use them properly. The guidelines listed above essentially amount to yes, you can use chatbots to help you write and improve your writing, but they should not be relied upon unquestioningly. That means human input and checking afterwards are indispensable. Also important is flagging up that LLMs were used in some way, so that users of Wikipedia know where information is coming from, and can be alert to possible problems arising from this fact.

The Wikipedia draft policy concentrates on how LLMs output might be used to create material for Wikipedia entries. The Vice article points out that there is another question, about whether there should be restrictions on how LLMs can use Wikipedia entries as part of the machine learning process:

The [Wikipedia] community is also divided on whether large language models should be allowed to train on Wikipedia content. While open access is a cornerstone of Wikipedias design principles, some worry the unrestricted scraping of internet data allows AI companies like OpenAI to exploit the open web to create closed commercial datasets for their models. This is especially a problem if the Wikipedia content itself is AI-generated, creating a feedback loop of potentially biased information, if left unchecked.

That concern seems overblown. Low-quality training materials can cause chatbots to produce questionable or downright harmful outputs. An obvious way to counter that would be to encourage the use of high-quality input that has undergone some kind of fact checking. Wikipedia is one of the best and largest sources of such material, and in hundreds of languages. Provided the final Wikipedia policy on LLMs requires human checks on chatbot output, as proposed in the draft, the use of Wikipedia articles for training LLMs should surely be encouraged with the aim of making chatbots better for everyone.

Follow me @glynmoody onMastodon.

Filed Under: chatbots, chatgpt, llms, wikipediaCompanies: openai

The rest is here:
Wikipedia Grapples With Chatbots: Should It Allow Their Use For ... - Techdirt