Working groups, gender, and publication impact of Canada’s ecology and evolution faculty
Bibliographic record
Abstract
Working groups are recognized as a highly effective method for synthesizing science. It is less clear if participating in working groups benefits individual researchers, or if benefits differ between men and women. This is a critical question, for the working group method is not sustainable if the benefit to science comes at a cost to academic careers or gender equity. Here we analyze the publications of Canadian university faculty specialized in ecology and evolution ( N = 1244), a field that has embraced the working group method. Researchers were more likely to have participated in a working group as their academic age and prior H-index increased, but controlling for these factors there was no effect of gender. Using a longitudinal analysis, we find that researcher H-indices accrue 14% faster following their first working group publication, regardless of gender. Part of this acceleration may be the 3- to 5-fold higher citation rate of working group synthesis publications. In a survey ( N = 169), researchers also report indirect benefits of working groups, at similar rates for men and women. Working groups are therefore good not just for science but also for scientists. Formalized mechanisms for collaborations such as working groups may also offset gender inequities in science.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".