Thanking the World: Exploring Gender-Based Differences in Acknowledgment Patterns and Support Systems in Theses

• URL: http://arxiv.org/abs/2406.06006v1
• Date: Mon, 10 Jun 2024 04:06:55 GMT
• Title: Thanking the World: Exploring Gender-Based Differences in Acknowledgment Patterns and Support Systems in Theses
• Authors: Manika Lamba, Hendrik Erz,
• Abstract summary: This paper investigates the sources of support for male and female researchers in completing their master's or doctoral theses. We utilize a novel method of extracting the various types of support systems that are acknowledged in 1252 ETDs using RoBERTa-based models.
• Score: 0.0
• License:
• Abstract: Research on acknowledgment sections of scientific papers has gained significant attention, but there remains a dearth of studies examining acknowledgments in the context of Electronic Theses and Dissertations. This paper addresses this gap by investigating the sources of support for male and female researchers in completing their master's or doctoral theses, focusing on the discipline of Library and Information Science. We utilize a novel method of extracting the various types of support systems that are acknowledged in 1252 ETDs using RoBERTa-based models. The most prominent forms of support acknowledged by researchers are academic, moral, financial, and religious support. While there are no significant gender-based differences in religious and financial support, the ratio of academic to moral support acknowledged by researchers shows strong gender-based variation. Additionally, advisors display a preference for supervising same-gender researchers. By comprehending the nuances of support systems and the unique challenges faced by researchers of different genders, we can foster a more inclusive and supportive academic environment. The insights gained from this research have implications for improving mentoring practices and promoting gender equality in academia.

Related papers

• Divided by discipline? A systematic literature review on the quantification of online sexism and misogyny using a semi-automated approach [1.1599570446840546]
  We present a semi-automated way to narrow down the search results in the different phases of selection stage in the PRISMA flowchart. We examine literature from computer science and the social sciences from 2012 to 2022. We discuss the challenges and opportunities for future research dedicated to measuring online sexism and misogyny.
  arXiv  Detail & Related papers  (2024-09-30T11:34:39Z)
• Inclusivity in Large Language Models: Personality Traits and Gender Bias in Scientific Abstracts [49.97673761305336]
  We evaluate three large language models (LLMs) for their alignment with human narrative styles and potential gender biases. Our findings indicate that, while these models generally produce text closely resembling human authored content, variations in stylistic features suggest significant gender biases.
  arXiv  Detail & Related papers  (2024-06-27T19:26:11Z)
• SurveyAgent: A Conversational System for Personalized and Efficient Research Survey [50.04283471107001]
  This paper introduces SurveyAgent, a novel conversational system designed to provide personalized and efficient research survey assistance to researchers. SurveyAgent integrates three key modules: Knowledge Management for organizing papers, Recommendation for discovering relevant literature, and Query Answering for engaging with content on a deeper level. Our evaluation demonstrates SurveyAgent's effectiveness in streamlining research activities, showcasing its capability to facilitate how researchers interact with scientific literature.
  arXiv  Detail & Related papers  (2024-04-09T15:01:51Z)
• Position: AI/ML Influencers Have a Place in the Academic Process [82.2069685579588]
  We investigate the role of social media influencers in enhancing the visibility of machine learning research. We have compiled a comprehensive dataset of over 8,000 papers, spanning tweets from December 2018 to October 2023. Our statistical and causal inference analysis reveals a significant increase in citations for papers endorsed by these influencers.
  arXiv  Detail & Related papers  (2024-01-24T20:05:49Z)
• Gender Inequalities: Women Researchers Require More Knowledge in Specific and Experimental Topics [1.4916971861796386]
  This study analyzes the relationship between regional and gender identities, topics, and knowledge status. We find that gender inequalities are merged with both regional-specific characteristics and global common patterns.
  arXiv  Detail & Related papers  (2023-09-05T05:36:06Z)
• Inferring gender from name: a large scale performance evaluation study [4.934579134540613]
  Researchers need to infer gender from readily available information, primarily from persons' names. Name-to-gender inference has generated an ever-growing domain of algorithmic approaches and software products. We conduct a large scale performance evaluation of existing approaches for name-to-gender inference. We propose two new hybrid approaches that achieve better performance than any single existing approach.
  arXiv  Detail & Related papers  (2023-08-22T13:38:45Z)
• Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach [77.61131357420201]
  We conduct a thorough and rigorous study on fairness disparities in peer review with the help of large language models (LMs) We collect, assemble, and maintain a comprehensive relational database for the International Conference on Learning Representations (ICLR) conference from 2017 to date. We postulate and study fairness disparities on multiple protective attributes of interest, including author gender, geography, author, and institutional prestige.
  arXiv  Detail & Related papers  (2022-11-07T16:19:42Z)
• Fairness in Recommender Systems: Research Landscape and Future Directions [119.67643184567623]
  We review the concepts and notions of fairness that were put forward in the area in the recent past. We present an overview of how research in this field is currently operationalized. Overall, our analysis of recent works points to certain research gaps.
  arXiv  Detail & Related papers  (2022-05-23T08:34:25Z)
• Towards Understanding Gender-Seniority Compound Bias in Natural Language Generation [64.65911758042914]
  We investigate how seniority impacts the degree of gender bias exhibited in pretrained neural generation models. Our results show that GPT-2 amplifies bias by considering women as junior and men as senior more often than the ground truth in both domains. These results suggest that NLP applications built using GPT-2 may harm women in professional capacities.
  arXiv  Detail & Related papers  (2022-05-19T20:05:02Z)
• Investigating writing style as a contributor to gender gaps in science and technology [0.0]
  We find significant differences in writing style by gender, with women using more involved features in their writing. Papers and patents with more involved features also tend to be cited more by women. Our findings suggest that scientific text is not devoid of personal character, which could contribute to bias in evaluation.
  arXiv  Detail & Related papers  (2022-04-28T22:33:36Z)
• Assessing Gender Bias in the Information Systems Field: An Analysis of the Impact on Citations [0.0]
  This paper outlines a study to estimate the impact of scholarly citations that female IS academics accumulate vis-a-vis their male colleagues. By doing so we propose to contribute knowledge on a core dimension of gender bias in academia, which is, so far, almost completely unexplored in the IS field.
  arXiv  Detail & Related papers  (2021-08-22T18:18:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.