The impact of gender on scientific writing: An observational study of grant proposals
Bibliographic record
Abstract
OBJECTIVES: This study aimed to determine whether there are differences in the language used in grant applications submitted to a Southern Brazil Research Support Foundation (FAPERGS) according to the gender, career stage, and the number of publications of applicants. STUDY DESIGN AND SETTING: This observational study also evaluated the relationship between gender, career stage, curriculum, and writing characteristics. Summaries of all research proposals in the biomedical field of FAPERGS during the years of 2013 and 2014 were evaluated according to six language patterns (Positive emotions, Negative emotions, Analytic thinking, Clout, Authenticity, and Emotional tone) defined by the LIWC software. Applicant's gender, career stage, and the number of publications were also collected. RESULTS: Three hundred and forty-four (344) grant proposals met the inclusion criteria and were included in the analysis. No statistical differences were observed in the language pattern used by different gender applicants. In the language used by successful and unsuccessful applicants, we only found a small difference for clout (score 54.5 for not funded and 56.5 for funded grants). However, the principal investigators of successful applications had a significantly higher number of papers published (mean number of papers: 104 versus 58.5). CONCLUSIONS: Gender bias appears to be a more complex problem than just the type of language used; the way society is organized causes several gender biases that may be reflected throughout the women's career.
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 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 | 0.016 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".