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Authorship disambiguation is crucial for advancing studies in science of science. However, assessing the quality of authorship disambiguation in large-scale databases remains challenging since it is difficult to manually curate a gold-standard dataset that contains disambiguated authors. Through estimating the timing of when 5.8 million biomedical researchers became independent Principal Investigators (PIs) with authorship metadata extracted from the OpenAlex -- the largest open-source bibliometric database -- we unexpectedly discovered an anomaly: over 60% of researchers appeared as the last authors in their first career year. We hypothesized that this improbable finding results from poor name disambiguation, suggesting that such an anomaly may serve as an indicator of low-quality authorship disambiguation. Our findings indicated that authors who lack affiliation information, which makes it more difficult to disambiguate, were far more likely to exhibit this anomaly compared to those who included their affiliation information. In contrast, authors with Open Researcher and Contributor ID (ORCID) -- expected to have higher quality disambiguation -- showed significantly lower anomaly rates. We further applied this approach to examine the authorship disambiguation quality by gender over time, and we found that the quality of disambiguation for female authors was lower than that for male authors before 2010, suggesting that gender disparity findings based on pre-2010 data may require careful reexamination. Our results provide a framework for systematically evaluating authorship disambiguation quality in various contexts, facilitating future improvements in efforts to authorship disambiguation.

Regulating AI is a key societal challenge, but which regulation methods are effective is unclear. This study measures the effectiveness of restricting AI services geographically, focusing on ChatGPT. OpenAI restricts ChatGPT access in several countries, including China and Russia. If restrictions are effective, ChatGPT use should be minimal in these countries. We measured use with a classifier based on distinctive word usage found in early versions of ChatGPT, e.g. "delve." We trained the classifier on pre- and post-ChatGPT "polished" abstracts and found it outperformed GPTZero and ZeroGPT on validation sets, including papers with self-reported AI use. Applying the classifier to preprints from Arxiv, BioRxiv, and MedRxiv showed ChatGPT was used in about 12.6% of preprints by August 2023, with 7.7% higher usage in restricted countries. The gap appeared before China's first major legal LLM became widely available. To test the possibility that, due to high demand, use in restricted countries would have been even higher without restrictions, we compared Asian countries with high expected demand (where English is not an official language) and found that use was higher in those with restrictions. ChatGPT use was correlated with higher views and downloads, but not citations or journal placement. Overall, restricting ChatGPT geographically has proven ineffective in science and possibly other domains, likely due to widespread workarounds., Comment: Three figures, two tables, 21 pages, and a 19-page appendix

We present the Chinese Dysarthria Speech Database (CDSD) as a valuable resource for dysarthria research. This database comprises speech data from 24 participants with dysarthria. Among these participants, one recorded an additional 10 hours of speech data, while each recorded one hour, resulting in 34 hours of speech material. To accommodate participants with varying cognitive levels, our text pool primarily consists of content from the AISHELL-1 dataset and speeches by primary and secondary school students. When participants read these texts, they must use a mobile device or the ZOOM F8n multi-track field recorder to record their speeches. In this paper, we elucidate the data collection and annotation processes and present an approach for establishing a baseline for dysarthric speech recognition. Furthermore, we conducted a speaker-dependent dysarthric speech recognition experiment using an additional 10 hours of speech data from one of our participants. Our research findings indicate that, through extensive data-driven model training, fine-tuning limited quantities of specific individual data yields commendable results in speaker-dependent dysarthric speech recognition. However, we observe significant variations in recognition results among different dysarthric speakers. These insights provide valuable reference points for speaker-dependent dysarthric speech recognition., Comment: 9 pages, 3 figures

Power dynamics influence every aspect of scientific collaboration. Team power dynamics can be measured by team power level and team power hierarchy. Team power level is conceptualized as the average level of the possession of resources, expertise, or decision-making authorities of a team. Team power hierarchy represents the vertical differences of the possessions of resources in a team. In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics. This research examines how team power dynamics affect team impact to fill the research gap. In this research, all co-authors of one publication are treated as one team. Team power level and team power hierarchy of one team are measured by the mean and Gini index of career age of co-authors in this team. Team impact is quantified by citations of a paper authored by this team. By analyzing over 7.7 million teams from Science (e.g., Computer Science, Physics), Social Sciences (e.g., Sociology, Library & Information Science), and Arts & Humanities (e.g., Art), we find that flat team structure is associated with higher team impact, especially when teams have high team power level. These findings have been repeated in all five disciplines except Art, and are consistent in various types of teams from Computer Science including teams from industry or academia, teams with different gender groups, teams with geographical contrast, and teams with distinct size.

Peer review is widely regarded as essential for advancing scientific research. However, reviewers may be biased by authors' prestige or other characteristics. Double-blind peer review, in which the authors' identities are masked from the reviewers, has been proposed as a way to reduce reviewer bias. Although intuitive, evidence for the effectiveness of double-blind peer review in reducing bias is limited and mixed. Here, we examine the effects of double-blind peer review on prestige bias by analyzing the peer review files of 5027 papers submitted to the International Conference on Learning Representations (ICLR), a top computer science conference that changed its reviewing policy from single-blind peer review to double-blind peer review in 2018. We find that after switching to double-blind review, the scores given to the most prestigious authors significantly decreased. However, because many of these papers were above the threshold for acceptance, the change did not affect paper acceptance decisions significantly. Nevertheless, we show that double-blind peer review may have improved the quality of the selections by limiting other (non-author-prestige) biases. Specifically, papers rejected in the single-blind format are cited more than those rejected under the double-blind format, suggesting that double-blind review better identifies poorer quality papers. Interestingly, an apparently unrelated change - the change of rating scale from 10 to 4 points - likely reduced prestige bias significantly, to an extent that affected papers' acceptance. These results provide some support for the effectiveness of double-blind review in reducing prestige bias, while opening new research directions on the impact of peer review formats.