Positive outcomes of COVID-19 research-related gender policy changes
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has exposed and exacerbated gender biases in science, technology, engineering, mathematics, and medicine. Accumulating evidence suggests that female scientists’ productivity dropped during the initial lockdown period. With more time being spent on caregiving responsibilities, women may be struggling to collaborate on grant applications and launch new experiments. Scientists with disabilities or who belong to Indigenous nations or communities of color may have less time to devote to research due to health, family, or community needs. Collateral damage in this situation, the appropriate integration of sex, gender, and other identity characteristics in research content may also suffer. Sex and gender are better attended to when female scientists form part of the research team. Research funding agencies have a role to play in mitigating these effects by putting in place gender equity policies that support all applicants and ensure research quality. Accordingly, a national health research funder implemented gender policy changes that included extending deadlines and factoring sex and gender into COVID-19 grant requirements. Following these changes, the funder received more applications from female scientists, awarded a greater proportion of grants to female compared to male scientists, and received and funded more grant applications that considered sex and gender in the content of COVID-19 research. Whether or not these strategies will be sufficient in the long-term to prevent widening of the gender gap in science, technology, engineering, mathematics and medicine requires continued monitoring and oversight. Further work is urgently required to mitigate inequities associated with identity characteristics beyond gender.
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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 | 0.067 | 0.140 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".