Gender Gap in Neurology Research Authorship (1946–2020)
Bibliographic record
Abstract
Gender disparity in the field of neurology impedes scientific advancements and innovations. In 2018, 45.0% of neurology and neurological subspecialty residents were women. Despite a notable rise in the proportion of women neurologists over the past decades, inequalities regarding publication proportions between men and women persist in the field. This cohort study examines authorship trends in articles published in 155 international neurology journals, identified as those listed in the annual Journal Citation Reports' “Clinical Neurology” section. Authors' names, authorship positions and countries of affiliation were extracted from PubMed for indexed articles published from 1946 to 2020. Gender-API (a validated and highly accurate application program interface) assigned binary genders to authors. Author gender proportions were compared across subspecialties, authorship position and years. In 303,385 unique articles, 1,663,036 total authors were identified of which 34.1% were women. Neuroradiology demonstrated the lowest proportion of women authors (21.3%), while neurogenetics displayed the highest (44.5%). In articles with multiple authors, both men and women last authors were more likely to publish with a male first author, though this was significantly more pronounced for men last authors (1.86 vs. 1.08; p < 0.001). From 2002 to 2020, women remained in the minority of last (24.6%), first (36.2%), and middle author positions (35.8%). The authorship gender distribution in neurological journals neither reflects the gender proportion of neurologists in the field overall nor in any subspecialty examined. We also find a tendency for senior and junior authors of the same gender to publish together which perpetuates authorship inequity. Further work is needed to identify underlying causes so that interventions might be developed to improve authorship diversity.
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 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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".