Bibliographic record
Abstract
The weight of evidence suggests that articles written by men and women receive citations at comparable rates. This suggests that research quality or gender-based bias in research evaluation and citing behaviors may not be the reason why academic women accumulate fewer citations than men at the career level. In this article, I outline a career perspective that highlights women's disadvantages in career progression as the root causes for the gender citation gap. I also consider how the gender citation gap may perpetuate the unequal pay between genders in science. My analysis of two different datasets, one including paper and citation information for over 130,000 highly cited scholars during the 1996-2020 period and another including citation and salary information for nearly 2,000 Canadian scholars over the 2014-2019 period, shows several important findings. First, papers written by women on average receive more citations than those written by men. Second, the gender citation gap grows larger with time as men and women progress in their careers, but the opposite pattern holds when research productivity and collaborative networks are considered. Third, higher citations lead to higher pay, and gender differences in citations explain a significant share of the gender wage gap. Findings demonstrate the critical need for more attention toward gender differences in career progression when investigating the causes and solutions for gender disparities in science.
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.
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 | 0.040 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".