Female citation impact superiority 1996-2018 in six out of seven\n English-speaking nations
Bibliographic record
Abstract
Efforts to combat continuing gender inequalities in academia need to be\ninformed by evidence about where differences occur. Citations are relevant as\npotential evidence in appointment and promotion decisions, but it is unclear\nwhether there have been historical gender differences in average citation\nimpact that might explain the current shortfall of senior female academics.\nThis study investigates the evolution of gender differences in citation impact\n1996-2018 for six million articles from seven large English-speaking nations:\nAustralia, Canada, Ireland, Jamaica, New Zealand, UK, and the USA. The results\nshow that a small female citation advantage has been the norm over time for all\nthese countries except the USA, where there has been no practical difference.\nThe female citation advantage is largest, and statistically significant in most\nyears, for Australia and the UK. This suggests that any academic bias against\nciting female authored research cannot explain current employment inequalities.\nNevertheless, comparisons using recent citation data, or avoiding it\naltogether, during appointments or promotion may disadvantage females in some\ncountries by underestimating the likely impact of their work, especially in the\nlong term.\n
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".