Policy proposals to promote inclusion of caregivers in the research funding system
Bibliographic record
Abstract
Research funding is critical for scientific production and career advancement in science, technology, engineering, mathematics, and medicine (STEMM). The COVID-19 pandemic has unmasked a deeply flawed research funding system riddled by inequitable policies, biased evaluations, and a lack of transparency and accountability. While most scientists were affected by the pandemic to some extent, evidence shows that women with caregiving responsibilities were disproportionately impacted, with long-term effects on their careers. However, despite calls for change by scientists globally, whose careers depend largely on funding success, decision-makers have made little to no effort to reform a funding system that marginalises a large proportion of researchers, including women, and especially mothers. Here, we review the current literature on gender bias in the STEMM funding process and propose a set of specific, actionable policies to promote caregiver inclusion and close the gender gap in research funding.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".