Gender differences in citation impact for 27 fields and six English-speaking countries 1996–2014
Bibliographic record
Abstract
Abstract Initiatives addressing the lack of women in many academic fields, and the general lack of senior women, need to be informed about the causes of any gender differences that may affect career progression, including citation impact. Previous research about gender differences in journal article citation impact has found the direction of any difference to vary by country and field, but has usually avoided discussions of the magnitude and wider significance of any differences and has not been systematic in terms of fields and/or time. This study investigates differences in citation impact between male and female first-authored research for 27 broad fields and six large English-speaking countries (Australia, Canada, Ireland, New Zealand, the UK, and the USA) from 1996 to 2014. The results show an overall female first author citation advantage, although in most broad fields it is reversed in all countries for some years. International differences include Medicine having a female first author citation advantage for all years in Australia, but a male citation advantage for most years in Canada. There was no general trend for the gender difference to increase or decrease over time. The average effect size is small, however, and unlikely to have a substantial influence on overall gender differences in researcher careers.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".