Though the concept of genre has been a subject of discussion for millennia, the relatively recent emergence of genre fiction has added a new layer to this ongoing conversation. While more traditional perspectives on genre have emphasized form, contemporary scholarship has invoked both formal and institutional characteristics in its taxonomy of genre, genre fiction, and literary fiction. This project uses computational methods to explore the soundness of genre as a formal designation as opposed to an institutional one. Pulling from Andrew Piper's CONLIT dataset of Contemporary Literature, we assemble a corpus of literary and genre fiction, with the latter category containing romance, mystery, and science fiction novels. We use Welch's ANOVA to compare the distribution of narrative features according to author gender within each genre and within genre versus literary fiction. Then, we use logistic regression to model the effect that each feature has on literary classification and to measure how author gender moderates these effects. Finally, we analyze stylistic and semantic vector representations of our genre categories to understand the importance of form and content in literary classification. This project finds statistically significant formal markers of each literary category and illustrates how female authorship narrows and blurs the target for achieving literary status.

These authors made history with their powerful books. March is Women's History Month, a time dedicated to honoring the powerful, inspiring and trailblazing women who have contributed amazing things to our world. What better way to celebrate this month than by diving into books written by women? Female authors have written a diverse range of books, from novels to memoirs, to science fiction and horror. Get your bookmarks ready and prepare to be captivated by these must-read books for Women's History Month. Follow an eccentric artist and her daughter through this short novel.

Large language models (LLMs) are increasingly utilized to assist in scientific and academic writing, helping authors enhance the coherence of their articles. Previous studies have highlighted stereotypes and biases present in LLM outputs, emphasizing the need to evaluate these models for their alignment with human narrative styles and potential gender biases. In this study, we assess the alignment of three prominent LLMs - Claude 3 Opus, Mistral AI Large, and Gemini 1.5 Flash - by analyzing their performance on benchmark text-generation tasks for scientific abstracts. We employ the Linguistic Inquiry and Word Count (LIWC) framework to extract lexical, psychological, and social features from the generated texts. Our findings indicate that, while these models generally produce text closely resembling human authored content, variations in stylistic features suggest significant gender biases. This research highlights the importance of developing LLMs that maintain a diversity of writing styles to promote inclusivity in academic discourse.

However, Prior research has investigated the benefits and costs of even when reviewers self-report as having the highest level double-anonymous review (DAR, also known as double-blind of expertise in their field, their guess accuracy is no better review) in comparison to single-anonymous review (SAR) and than those who are self-reported as less knowledgeable [17]. Several review papers have attempted to Increased editor burden in handling conflict of interest, author compile experimental results in peer review research both burden in anonymizing the manuscript, and reviewer burden broadly and in engineering and computer science specifically in navigating prior work by others and by the authors are also [1-4]. This document summarizes prior research in peer review cited as costs to DAR. that may inform decisions about the format of peer review in Despite these challenges, numerous robotics conferences the field of robotics and makes some recommendations for have already made the shift to DAR, including RSS and a potential next steps for robotics publications. Furthermore, top machine learning conferences such as NeurIPS and CoRL have II. The presence of gender bias and effect of DAR on such bias is a common concern in research into peer review but Based on the current literature, we find that the evidence the conclusions are varied. Many studies do conclude that in support of double-anonymous review is not sufficient to gender can disadvantage authors, particularly women [5, 6] conclusively recommend for implementation in robotics conferences and that DAR can reduce this bias [7].

Bengaluru stood fifth in the list of 50 top cities for having one of the world's largest diversity of Artificial Intelligence (A.I.) workers. The top four cities are from the U.S., including New York, San Francisco, Boston, and Seattle. San Francisco has four times the A.I. activity of other top cities with A.I. clusters. The ranking has been given by the TIDE Framework and listed by Harvard Business Review (HBR). The cities have been evaluated using a framework - TIDE (for Talent pool; Investments; Diversity of talent; Evolution of the country's digital foundations).

This article presents a large-scale automated analysis of gender trends in the authorship of Computer Science literature. We answer these questions by performing an automated study of literature metadata from scientific conferences and journals, using data from the Semantic Scholar academic search engine.a Our study incorporates metadata from 11.8M Computer Science publications. To provide a basis for comparison, we also analyze more than 140M articles from other fields of study. Our results demonstrate that although progress has been made, there is still a significant gap in gender representation among Computer Science authors. Continued delay in addressing the gender gap may perpetuate imbalances for generations to come. Our analysis was performed over the Semantic Scholar literature corpus.2 The corpus contains publications between 1940 and the end of November 2019, and associated metadata such as title, abstract, authors, publication venue, and year of publication.

Women are underrepresented in the fields of science, technology, engineering, and mathematics (STEM) in most countries, including Germany and the U.S.29,32 This was demonstrated in several surveys investigating the proportion of women in the STEM fields for specific populations. Some of these studies, for example, investigated the number of enrolled students10,30 or the percentage of female professors at universities. Other studies analyzed the disparities in research funding.23 Nearly all these surveys selected a particular population of women in consideration of their university degree or their nationality.11,34 Like many other studies investigating the gender gap and its reasons in science, these surveys are usually based on data records from several kinds of registrations or enrollments, for example, the enrollment as student or doctoral student, the registration of finished doctoral theses or the membership as professor in a certain country.1,14,16,28 However, researchers at the postdoctoral level or industrial researchers are often not registered and unfortunately drop out of the surveys.

From improved medical applications to self-driving cars and smart cities, AI has the potential to transform our digital, physical, and social environments in unprecedented ways and at an unprecedented speed. However, the same technologies can be used for mass surveillance, computational propaganda, and biased, discriminating decision-making. It is generally believed that increasing the diversity of the workforce developing AI systems will reduce the risk that they generate discriminatory and unfair outcomes, thus ensuring that their benefits are more widely shared. But how diverse is the workforce of the AI sector? We conducted a large-scale analysis of gender diversity in AI research using publications from arXiv, a widely-used preprints repository where we have identified AI papers through an expanded keyword analysis and predicted author gender using a name-to-gender inference service.

Information extraction involves classifying data items that are stored in plain text, and is a major area of research for machine learning scientists. Last week, a research team from MIT introduced a new approach to information extraction for machine learning systems at the Association for Computational Linguistics' Conference on Empirical Methods on Natural Language Processing, and won a best-paper award. Instead of feeding their system as much data as possible, the team's winning approach takes a different route and focuses on a much smaller data set, a similar process used by human beings – if you're reading a paper that you don't understand, you're likely to do a search on the web and find articles that you are able to understand. This new system approach does something similar; if the system's confidence score is low in assessing a particular text, it will query for more information, pulling up a handful of new articles from the web that correlate with a specific set of terms. In future, this model could be applied to sparse data and save much time in reviewing databases.

While the number of women who choose to pursue computer science and engineering careers is growing, men continue to largely outnumber them. In this paper, we describe a data mining approach that relies on a large collection of scientific articles to identify differences in gender interests in this field. Our hope is that through a better understanding of the differences between male and female preferences, we can enable more effective outreach and retention, and consequently contribute to the growth of the number of women who choose to pursue careers in this field.