Perceiving gendered organizations: positions, power, and gender in geoscience
Bibliographic record
Abstract
This article explores inequality in European geoscience organizations through the perspective of geoscience women professionals and their perception of gendered positions in academia and industry. Male dominance in geoscience organizations has previously been demonstrated within US and Canadian organizations, often in relation to gender inequality in STEM subjects and rarely in relation to the specific ideals and practices that shape geoscience. The current study contributes a European context, as well as a comparative approach to gendered positions in the organizational contexts of academia and industry. Using participatory research methods and visualization techniques, the study collected 42 organizational maps of academic and industry organizations in 16 European countries. The results reveal perceptions of gender inequality in academic and industrial geoscience organizations through women’s limited access to positions of power, i.e. women geoscience professionals perceived underrepresentation in senior management positions in industry and in senior positions in academic organizations. Within the growing demand for geoscience expertise in the green transition, the results raise questions about what the perceived structures of gender inequality mean in relation to sustainable employment and good working conditions in European geoscience.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".