Gender-based citation differences in speech–language pathology
Bibliographic record
Abstract
Gender inequalities are well documented in science and typically favor male scientists. A particularly pervasive gender difference is undercitation of publications authored by women, resulting in profound negative effects on academic visibility and career advancement. This inequality has been documented in fields where author gender distributions are strongly skewed towards men (astronomy, physics, neuroscience). By investigating citation practices in a field that has traditionally been more accessible to female scientists (speech–language pathology, SLP), we demonstrate that gendered citation practices are mediated by author gender distribution in specific fields, rather than being a universal pattern. Specifically, our results revealed a citation pattern in SLP that overall tends to favor female authors, persists after controlling for potential confounding factors and, is particularly strong when female authors are citing publications involving female first and senior author teams. Our results suggest that the implementation of effective measures to increase the number and influence of underrepresented individuals in specific fields of science may contribute to mitigate downstream disadvantages for career advancement. However, future research in fields with different author gender distributions and consideration of additional mediating factors is required to establish a potential causal link between field specific authorship patterns and gendered citation inequality.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".