Construction of a bibliometric dataset on gender studies

Authors

DOI:

https://doi.org/10.56294/piii2024316

Keywords:

Gender Studies, Bibliometrics, Topic Modeling

Abstract

Feminist discussions are reflected in scientific production.(1) During the 1970s, various lines of research from different disciplines were institutionalized, conforming gender studies.(2,3) While "gender studies" often denotes a field, it can be understood as an umbrella term for a diversity of approaches. From other disciplines, a gender perspective can be adopted, recognizing the constitution of power relations between genders and considering their concrete expressions in different areas.

Given the diffusion of this approach in multiple disciplines, its bibliometric approach presents difficulties. New computational tools can contribute to identifying publications linked to gender studies even when they are indexed as part of other disciplines. In this work, we applied bibliometric techniques and natural language processing to the Dimensions database to build a dataset of scientific publications that allows us to analyze gender studies and their influence on other disciplines.(4–6) This is achieved through a hybrid methodology that combines core journals and keywords in titles.

We obtained a set of more than 1.5 million publications, in English, Spanish, French, and Portuguese, published between 1970 and 2020. It allows characterizing gender studies according to citations and collaborations, the participation of institutions, countries and regions, or the evolution of certain concepts or theories. The methodology used would be suitable for studying other cases that exceed disciplinary limits, such as schools of thought or political movements that have become scientific disciplines

References

1. Harding S. Whose Science? Whose Knowledge?: Thinking from Women’s Lives. Cornell University Press; 1991. 334 p.

2. Suárez Tomé D. Introducción a la teoría feminista [Internet]. Nido De Vacas; 2022 [cited 2023 Jul 24]. Available from: https://ri.conicet.gov.ar/handle/11336/202459

3. Richardson SS. Feminist philosophy of science: history, contributions, and challenges. Synthese [Internet]. 2010 [cited 2023 Oct 31];177(3):337–62. Available from: https://www.jstor.org/stable/40985708

4. Sugimoto CR, Larivière V. Measuring Research: What Everyone Needs to Know. Oxford University Press; 2018. 169 p.

5. Grootendorst M. BERTopic: Neural topic modeling with a class-based TF-IDF procedure [Internet]. arXiv; 2022 [cited 2024 Feb 23]. Available from: http://arxiv.org/abs/2203.05794

6. Herzog C, Hook D, Konkiel S. Dimensions: Bringing down barriers between scientometricians and data. Quant Sci Stud [Internet]. 2020 Feb 1 [cited 2024 Feb 23];1(1):387–95. Available from: https://doi.org/10.1162/qss_a_00020

Downloads

Published

2024-05-16

How to Cite

1.
Shokida NS, Kozlowski D, Larivière V. Construction of a bibliometric dataset on gender studies. SCT Proceedings in Interdisciplinary Insights and Innovations [Internet]. 2024 May 16 [cited 2024 Oct. 12];2:316. Available from: https://proceedings.ageditor.ar/index.php/piii/article/view/327