WHY DO FEWER WOMEN THAN MEN APPLY FOR GRANTS AFTER THEIR PHDS?
Bibliographic record
Abstract
In spring 2013, the Society for American Archaeology created the Task Force on Gender Disparities in Archaeological Grant Submissions because of an apparent disparity in the rates of senior (post-PhD) proposal submissions by men and women to archaeology programs at the National Science Foundation (NSF) and the Wenner-Gren Foundation for Anthropological Research. Although NSF success rates for men and women between 2009 and 2013 were roughly equal, the number of senior women archaeology submissions was half that of men. Given the documented increase in the proportion of women in academic archaeology, this representation of women seemed low. Moreover, submissions for NSF doctoral dissertation improvement grants were evenly divided between men and women. Statistics for Wenner-Gren noted the same general disparity in archaeology. This study examines and integrates a variety of data sources, including interviews with post-PhD women, to determine whether or not there is a problem in research grant submissions. Although the results indicate that there is a problem, it is multifaceted. Women are not well represented at research-intensive universities, and some women instead practice what we term “scaffolding” to integrate smaller pots of money to accomplish their research. Recommendations are provided for female applicants, academic departments, the Society for American Archaeology, and granting agencies.
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".