Construction of a bibliometric dataset on gender studies
Bibliographic record
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.072 | 0.088 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".