Working groups, gender and publication impact of Canada’s ecology and evolution faculty
Bibliographic record
Abstract
ABSTRACT A critical part of science is the extraction of general principles by synthesizing results from many different studies or disciplines. In the fields of ecology and evolution, a popular method to conduct synthesis science is in working groups – that is, research collaborations based around intensive week-long meetings. We present in this report an analysis of the impact of working group participation and gender on the publication impact of ecology and evolution faculty at Canadian universities who were research active over the last three decades (N=1408). Women are underrepresented in this research population relative to the general population, and even the Canadian faculty population. Participation in working groups not only benefits science, but also benefits the researchers involved by accelerating the temporal increase in their H-index. However, this benefit is particularly driven by senior male researchers. The effect is weaker for female researchers and even negative for researchers within 4 years of their PhD. However, gender does not affect current participation rates in working groups, nor reported indirect benefits – such as future collaborations, funding and data resources. The results of this study suggest that working groups can act as career catalysts for researchers, but that – as in many areas of science – there are challenging issues of equity that require action. Because the H-index is a cumulative measure, gender inequities from before the turn of the millenium may still be distorting the perceived publication impact of today’s research-active faculty.
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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".