Bibliographic record
Abstract
Abstract Do women face a disadvantage in terms of citation rates, and if so, in what ways? This article provides a comprehensive overview of existing research on the relationship between gender and citations. Three distinct approaches are identified: (1) per‐article approach that compares gender differences in citations between articles authored by men and women, (2) per‐author approach that compares the aggregate citation records of men and women scholars over a specified period or at the career level, and (3) reference‐ratio approach that assesses the gender distribution of references in articles written by men and women. I show that articles written by women receive comparable or even higher rates of citations than articles written by men. However, women tend to accumulate fewer citations over time and at the career level. Contrary to the notion that women are cited less per article due to gender‐based bias in research evaluation or citing behaviors, this study suggests that the primary reason for the lower citation rates at the author level is women publishing fewer articles over their careers. Understanding and addressing the gender citation gap at the author level should therefore focus on women's lower research productivity over time and the contributing factors. To conclude, I discuss the potential detrimental impact of lower citations on women's career progression and the ways to address the issue to mitigate gender inequalities 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 | 0.022 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".