Knowledge Production: Analysing Gender- and Country-Dependent Factors in Research Topics through Term Communities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Scholarly publications are among the most tangible forms of knowledge production. Therefore, it is important to analyse them, amongst other features, for gender or country differences and the incumbent inequalities. While there are many quantitative studies of publication activities and success in terms of publication numbers and citation counts, a more content-related understanding of differences in the choice of research topics is rare. The present paper suggests an innovative method of using term communities in co-occurrence networks for detecting and evaluating the gender- and country-specific distribution of topics in research publications. The method is demonstrated with a pilot study based on approximately a quarter million of publication abstracts in seven diverse research areas. In this example, the method validly reconstructs all obvious topic preferences, for instance, country-dependent language-related preferences. It also produces new insight into country-specific research focuses. It emerges that in all seven subject areas studied, topic preferences are significantly different depending on whether all authors are women, all authors are men, or there are female and male co-authors, with a tendency of male authors towards theoretical core topics, of female authors towards peripheral applied topics, and of mixed-author teams towards modern interdisciplinary topics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it