Empirical mixed-methods study finds CS venues with accessible name change policies show fewer citation errors (899 vs 996 per 1,000 papers) and 92% drop in deadnaming of transgender researchers from 2019-2024.
arXiv preprint arXiv:2411.05025 , year=
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LLMs given only research questions from 1000 arXiv CS papers recommend a narrower set of methods than the original papers, with effective model-entity diversity dropping from 1232 to 59-96 and stronger agreement among LLMs than with papers.
A think-aloud study reveals that AI tools in early research misrepresent uncertainty, obscure provenance, and create fragile trust, leading researchers to develop compensatory strategies to preserve scholarly judgment.
LLMs fail to recognize most retracted articles from titles and abstracts alone, with over 80% error rate, but have low false positive rates on non-retracted articles.
Reddit data analysis shows reply-based mobile scams growing nearly twice as fast as click-based ones while evading commercial and open-source detectors.
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Making a Name for Myself: On Academic Naming Policies and their Impact
Empirical mixed-methods study finds CS venues with accessible name change policies show fewer citation errors (899 vs 996 per 1,000 papers) and 92% drop in deadnaming of transgender researchers from 2019-2024.
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Thinking Like a Scientist? A Structural Study of LLM-Generated Research Methods
LLMs given only research questions from 1000 arXiv CS papers recommend a narrower set of methods than the original papers, with effective model-entity diversity dropping from 1232 to 59-96 and stronger agreement among LLMs than with papers.
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How Researchers Navigate Accountability, Transparency, and Trust When Using AI Tools in Early-Stage Research: A Think-Aloud Study
A think-aloud study reveals that AI tools in early research misrepresent uncertainty, obscure provenance, and create fragile trust, leading researchers to develop compensatory strategies to preserve scholarly judgment.
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Do Large Language Models know Which Published Articles have been Retracted?
LLMs fail to recognize most retracted articles from titles and abstracts alone, with over 80% error rate, but have low false positive rates on non-retracted articles.
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Reddit data analysis shows reply-based mobile scams growing nearly twice as fast as click-based ones while evading commercial and open-source detectors.