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Record W2793892262 · doi:10.1017/aaq.2017.73

WHY DO FEWER WOMEN THAN MEN APPLY FOR GRANTS AFTER THEIR PHDS?

2018· article· en· W2793892262 on OpenAlexaff
Lynne Goldstein, Barbara J. Mills, Sarah Herr, Jo Burkholder, Leslie C. Aiello, Christopher I. Thornton

Bibliographic record

VenueAmerican Antiquity · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical and Cultural Archaeology Studies
Canadian institutionsAdidas (Canada)
FundersWenner-Gren FoundationNational Geographic SocietyNational Science Foundation
KeywordsRepresentation (politics)Foundation (evidence)Task forceLibrary scienceVariety (cybernetics)SociologyHistoryPolitical scienceArchaeologyPublic administrationLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.296
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations43
Published2018
Admission routes1
Has abstractyes

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