Exploring Sex and/or Gender Disparities in STEMM Research Grant Funding
Bibliographic record
Abstract
We’re aiming to comprehensively explore sex and/or gender disparities in research grant funding within the fields of Science, Technology, Engineering, Mathematics, and Medicine (STEMM). Recognising that research funding serves as a cornerstone for academic and professional advancement in STEMM, we use a multi-methods approach to understand how sex and/or gender dynamics influence the allocation of research funding. Using scoping review methods, we begin with an exploratory scan of MEDLINE and extend our search to Embase (Ovid), PsycINFO, and Web of Science. We also delve into grey literature platforms like OpenGrey and GoogleScholar. The inclusion criteria for the review are stringent, focusing on studies that explicitly report outcomes based on the sex and/or gender of researchers and provide data on grant applications, research funding awards, and the amount of funding awarded. We will present our findings using data visualization tools, to illustrate the distribution of studies by year, STEMM area, study design, and sex and/or gender. We want to identify and characterise gaps in the understanding of the grant application landscape in STEMM, both within our research team and the broader academic community. Using semi-structured interviews, we explore the lived experiences, challenges, and perceptions of women researchers who lead health research grant applications. We will delve into the factors that women researchers consider when recruiting a research team and budgeting. Our study will examine these investigators' considerations in the grant funding planning stages, budgeting, staff recruitment, and retention. We’re seeking to uncover the nuanced decision-making processes and considerations that shape the budgeting strategies of women in research leadership roles.
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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 | 0.125 | 0.260 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".