Gender differences in representation, citations, and h-index: An empirical examination of the field of communication across the ten most productive countries
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
Women researchers have been shown to be underrepresented in science, especially among the most productive scholars. This is especially relevant in the social sciences and humanities fields, where gender parity is closer, but disparities among top scholars are still pronounced. The gender gap in the field of communication has been explored from several approaches, but studies focusing on gender differences in representation, citations, and h-index are rather scarce. Drawing upon data retrieved from SciVal, we conducted a comparative study of the top 500 and top 100 most productive scholars (N = 5000) for each of the ten most productive countries in communication research in the 2019-2022 period: the United States, the United Kingdom, China, Spain, Germany, India, Australia, Canada, Italy, and the Netherlands. The results indicate a consistent underrepresentation of women, particularly among the top 500, across countries. Despite women being cited more frequently than men in some countries over shorter time frames, a gender bias persists favoring men, particularly when considering the h-index. All in all, our study shows that, despite hints of gender equality in citation patterns, the gender gap still constitutes a structural part of the field of communication when addressing gender representation in research productivity and long-term dynamics of research impact.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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