In this project, we measured overconfidence in a large, heterogeneous sample making familiar, repeated choices in a natural environment that provided direct feedback. Specifically, in study 1, we elicited predictions of own finishing time among participants of the 2012 Warsaw Marathon. The participants' prediction errors were highly correlated with the change in pace over the course of the run: overly optimistic forecasters slowed down more in the second half. In study 2, we consequently took this slowdown as a proxy for overconfidence and used existing field data of one million participants in several large marathons for which split times are available (but own predictions are not). Both studies indicate that men as well as the youngest and oldest participants tend to be more confident. In study 2, we were able to investigate national and cultural dimensions. We found confirmation of previously reported findings of relative overconfidence in Asians. Additionally, we show some largely novel results, in particular that relatively conservative societies tend to be relatively overconfident. Copyright © 2016 John Wiley & Sons, Ltd.

This article explores the relationship between linguistic culture and the preferred standards of presenting information based on article representation in major Wikipedias. Using primary research analysis of the number of images, references, internal links, external links, words, and characters, as well as their proportions in Good and Featured articles on the eight largest Wikipedias, we discover a high diversity of approaches and format preferences, correlating with culture. We demonstrate that high‐quality standards in information presentation are not globally shared and that in many aspects, the language culture's influence determines what is perceived to be proper, desirable, and exemplary for encyclopedic entries. As a result, we demonstrate that standards for encyclopedic knowledge are not globally agreed‐upon and “objective” but local and very subjective.

BackgroundWikipedia, the multilingual encyclopedia, was founded in 2001 and is the world’s largest and most visited online general reference website. It is widely used by health care professionals and students. The inclusion of journal articles in Wikipedia is of scholarly interest, but the time taken for a journal article to be included in Wikipedia, from the moment of its publication to its incorporation into Wikipedia, is unclear.ObjectiveWe aimed to determine the ranking of the most cited journals by their representation in the English-language medical pages of Wikipedia. In addition, we evaluated the number of days between publication of journal articles and their citation in Wikipedia medical pages, treating this measure as a proxy for the information-diffusion rate.MethodsWe retrieved the dates when articles were included in Wikipedia and the date of journal publication from Crossref by using an application programming interface.ResultsFrom 11,325 Wikipedia medical articles, we identified citations to 137,889 journal articles from over 15,000 journals. There was a large spike in the number of journal articles published in or after 2002 that were cited by Wikipedia. The higher the importance of a Wikipedia article, the higher was the mean number of journal citations it contained (top article, 48.13 [SD 33.67]; lowest article, 6.44 [SD 9.33]). However, the importance of the Wikipedia article did not affect the speed of reference addition. The Cochrane Database of Systematic Reviews was the most cited journal by Wikipedia, followed by The New England Journal of Medicine and The Lancet. The multidisciplinary journals Nature, Science, and the Proceedings of the National Academy of Sciences were among the top 10 journals with the highest Wikipedia medical article citations. For the top biomedical journal papers cited in Wikipedia's medical pages in 2016-2017, it took about 90 days (3 months) for the citation to be used in Wikipedia.ConclusionsWe found evidence of “recentism,” which refers to preferential citation of recently published journal articles in Wikipedia. Traditional high-impact medical and multidisciplinary journals were extensively cited by Wikipedia, suggesting that Wikipedia medical articles have robust underpinnings. In keeping with the Wikipedia policy of citing reviews/secondary sources in preference to primary sources, the Cochrane Database of Systematic Reviews was the most referenced journal.

The performance of text classification methods has improved greatly over the last decade for text instances of less than 512 tokens. This limit has been adopted by most state-of-the-research transformer models due to the high computational cost of analyzing longer text instances. To mitigate this problem and to improve classification for longer texts, researchers have sought to resolve the underlying causes of the computational cost and have proposed optimizations for the attention mechanism, which is the key element of every transformer model. In our study, we are not pursuing the ultimate goal of long text classification, i.e., the ability to analyze entire text instances at one time while preserving high performance at a reasonable computational cost. Instead, we propose a text truncation method called Text Guide, in which the original text length is reduced to a predefined limit in a manner that improves performance over naive and semi-naive approaches while preserving low computational costs. Text Guide benefits from the concept of feature importance, a notion from the explainable artificial intelligence domain. We demonstrate that Text Guide can be used to improve the performance of recent language models specifically designed for long text classification, such as Longformer. Moreover, we discovered that parameter optimization is the key to Text Guide performance and must be conducted before the method is deployed. Future experiments may reveal additional benefits provided by this new method.

The authors wanted to verify a popular belief that women scholars have been disproportionately affected by the COVID-19 pandemic. We studied the first names of authors of 266,409 articles from 2813 journals in 21 disciplines, and we found no significant differences between men and women in publication patterns between 2021, 2020, and 2019 overall. However, we found significant differences in publication patterns between gender in different disciplines. In addition, in disciplines where the proportion of women authors is higher, there are fewer single-authored articles. In the multi-author articles if the first author is female, there is more gender balance among authors, although there are still fewer women co-authors.