Gender and Arctic climate change science in Canada
Bibliographic record
Abstract
Abstract There is growing recognition that gender diversity within research organizations can result in innovative research outcomes. It has also been recognized that gender homogeneity can undermine the quality and breadth of the research and may allow some to cast doubt on the legitimacy of scientific findings. In this paper, we present the results of a gender-based analysis of Canada’s ArcticNet Networks Centers of Excellence. Representing Canada’s single largest commitment to climate change science, ArcticNet has involved 761 researchers who have published >2400 peer-reviewed publications on the impacts of climate change in the Canadian Arctic. Our results indicate that, despite outnumbering their male peers at the graduate levels, the representation of women within ArcticNet exhibits a marked decline to only 21% (N = 51) of all ArcticNet investigators (N = 246). In addition to being numerically under-represented, female investigators in ArcticNet have fewer research collaborators and are generally less integrated into the network as compared to their male colleagues. Male investigators tend to form homophilious ties—publishing predominately with other males, whereas female investigators have heterophilious collaborations, with fewer peer-reviewed journal articles. Given the complexities of climate change research, particularly in the Arctic where the impacts of climate change are projected to be most extreme, the equitable inclusion of female scientists and other under-represented groups is crucial if sustainable solutions are to be found.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".