Delineating Feminist Studies through bibliometric analysis
- URL: http://arxiv.org/abs/2411.18306v1
- Date: Wed, 27 Nov 2024 12:52:51 GMT
- Title: Delineating Feminist Studies through bibliometric analysis
- Authors: Natsumi S. Shokida, Diego Kozlowski, Vincent Larivière,
- Abstract summary: This paper proposes a novel approach for identifying gender/sex related publications scattered across diverse scientific disciplines.
We employ bibliometric techniques, natural language processing (NLP) and manual curation to compile a dataset of scientific publications.
The resulting dataset comprises over 1.9 million scientific documents published between 1668 and 2023, spanning four languages.
- Score: 1.1060425537315088
- License:
- Abstract: The multidisciplinary and socially anchored nature of Feminist Studies presents unique challenges for bibliometric analysis, as this research area transcends traditional disciplinary boundaries and reflects discussions from feminist and LGBTQIA+ social movements. This paper proposes a novel approach for identifying gender/sex related publications scattered across diverse scientific disciplines. Using the Dimensions database, we employ bibliometric techniques, natural language processing (NLP) and manual curation to compile a dataset of scientific publications that allows for the analysis of Gender Studies and its influence across different disciplines. This is achieved through a methodology that combines a core of specialized journals with a comprehensive keyword search over titles. These keywords are obtained by applying Topic Modeling (BERTopic) to the corpus of titles and abstracts from the core. This methodological strategy, divided into two stages, reflects the dynamic interaction between Gender Studies and its dialogue with different disciplines. This hybrid system surpasses basic keyword search by mitigating potential biases introduced through manual keyword enumeration. The resulting dataset comprises over 1.9 million scientific documents published between 1668 and 2023, spanning four languages. This dataset enables a characterization of Gender Studies in terms of addressed topics, citation and collaboration dynamics, and institutional and regional participation. By addressing the methodological challenges of studying "more-than-disciplinary" research areas, this approach could also be adapted to delineate other conversations where disciplinary boundaries are difficult to disentangle.
Related papers
- Revisiting gender bias research in bibliometrics: Standardizing methodological variability using Scholarly Data Analysis (SoDA) Cards [4.7078204693473795]
We propose the development and implementation of Scholarly Data Analysis (SoDA) Cards.
These cards will provide a structured framework for documenting and reporting key methodological choices in scholarly data analysis.
arXiv Detail & Related papers (2025-01-30T04:22:50Z) - Gender assignment in doctoral theses: revisiting Teseo with a method based on cultural consensus theory [0.0]
This study critically evaluates gender assignment methods within academic contexts.
The research introduces nomquamgender, a cultural consensus-based method, and applies it to Teseo, a Spanish dissertation database.
arXiv Detail & Related papers (2025-01-20T15:22:01Z) - 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) - 15 Years of Algorithmic Fairness -- Scoping Review of Interdisciplinary Developments in the Field [0.0]
This paper presents a scoping review of algorithmic fairness research over the past fifteen years.
All articles come from the computer science and legal field and focus on AI algorithms with potential discriminatory effects on population groups.
arXiv Detail & Related papers (2024-07-23T07:50:01Z) - 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) - Unveiling Gender Bias in Terms of Profession Across LLMs: Analyzing and
Addressing Sociological Implications [0.0]
The study examines existing research on gender bias in AI language models and identifies gaps in the current knowledge.
The findings shed light on gendered word associations, language usage, and biased narratives present in the outputs of Large Language Models.
The paper presents strategies for reducing gender bias in LLMs, including algorithmic approaches and data augmentation techniques.
arXiv Detail & Related papers (2023-07-18T11:38:45Z) - 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) - Revise and Resubmit: An Intertextual Model of Text-based Collaboration
in Peer Review [52.359007622096684]
Peer review is a key component of the publishing process in most fields of science.
Existing NLP studies focus on the analysis of individual texts.
editorial assistance often requires modeling interactions between pairs of texts.
arXiv Detail & Related papers (2022-04-22T16:39:38Z) - What's New? Summarizing Contributions in Scientific Literature [85.95906677964815]
We introduce a new task of disentangled paper summarization, which seeks to generate separate summaries for the paper contributions and the context of the work.
We extend the S2ORC corpus of academic articles by adding disentangled "contribution" and "context" reference labels.
We propose a comprehensive automatic evaluation protocol which reports the relevance, novelty, and disentanglement of generated outputs.
arXiv Detail & Related papers (2020-11-06T02:23:01Z) - Positioning yourself in the maze of Neural Text Generation: A
Task-Agnostic Survey [54.34370423151014]
This paper surveys the components of modeling approaches relaying task impacts across various generation tasks such as storytelling, summarization, translation etc.
We present an abstraction of the imperative techniques with respect to learning paradigms, pretraining, modeling approaches, decoding and the key challenges outstanding in the field in each of them.
arXiv Detail & Related papers (2020-10-14T17:54:42Z) - A Survey on Text Classification: From Shallow to Deep Learning [83.47804123133719]
The last decade has seen a surge of research in this area due to the unprecedented success of deep learning.
This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021.
We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification.
arXiv Detail & Related papers (2020-08-02T00:09:03Z)
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.