Gender underlies the formation of STEM research groups
Bibliographic record
Abstract
Abstract Research groups are the cornerstone of scientific research, yet little is known about how these groups are formed and how their organization is influenced by the gender of the research group leader. This represents an important gap in our understanding of the processes shaping gender structure within universities and the academic fields they represent. Here, we report the results of an email survey sent to department chairs and discipline‐specific listservs. We received responses from 275 female and 175 male research group leaders. Most respondents were biologists ( n = 328) but psychology ( n = 27), chemistry ( n = 16), physics ( n = 32), and mathematics ( n = 30) were also relatively well represented. We found that men were self‐reported as overrepresented in research groups in the physical sciences, particularly at later career stages. Within biology, male and female group leaders reported supervising a disproportionate number of same‐gender trainees (students and postdoctoral fellows), particularly early in their careers. These self‐reported patterns were driven primarily by gender‐based differences in the pool of students applying to their research groups, while gender differences in acceptance rates played a seemingly smaller role. We discuss the implications of our results for women continuing into the professoriate and for the recruitment of young scientists into research groups.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".