Was the COVID-19 Pandemic Associated with Gender Disparities in Authorship of Manuscripts Submitted to Clinical Neuropsychology Journals?
Bibliographic record
Abstract
OBJECTIVE: The COVID-19 pandemic exacerbated gender disparities in some academic disciplines. This study examined the association of the pandemic with gender authorship disparities in clinical neuropsychology (CN) journals. METHOD: Author bylines of 1,018 initial manuscript submissions to four major CN journals from March 15 through September 15 of both 2019 and 2020 were coded for binary gender. Additionally, authorship of 40 articles published on pandemic-related topics (COVID-19, teleneuropsychology) across nine CN journals were coded for binary gender. RESULTS: Initial submissions to these four CN journals increased during the pandemic (+27.2%), with comparable increases in total number of authors coded as either women (+23.0%) or men (+25.4%). Neither the average percentage of women on manuscript bylines nor the proportion of women who were lead and/or corresponding authors differed significantly across time. Moreover, the representation of women as authors of pandemic-related articles did not differ from expected frequencies in the field. CONCLUSIONS: Findings suggest that representation of women as authors of peer-reviewed manuscript submissions to some CN journals did not change during the initial months of the COVID-19 pandemic. Future studies might examine how risk and protective factors may have influenced individual differences in scientific productivity during the pandemic.
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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 | 0.008 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".