Introduction to the Special Issue on Gender and Geoethics in the Geosciences
Bibliographic record
Abstract
In this introduction to the Special Issue on Gender and Geoethics in the Geosciences is a focus on the participation of women in traditionally male-dominated professions, with geography as an exemplary academic subject. The Special Issue stems from the Commission of Gender and Geoethics as part of the International Association of Geoethics, and endeavors to bring together efforts at various spatial scales that examine the position of women in science and engineering in particular, as conveyed in engineering geology, disaster management sciences, and climate change adaptation studies. It has been discovered, for instance, that men are more active and personally prepared at the community level (in Atlantic Canada coastal communities), and more action is still required in developing countries especially to promote gender equality and empower women. Studies contained in this Special Issue also reveal that tutoring and mentoring by other women can promote further involvement in non-traditional professions, such as professional engineering geology, where women are preferring more traditional (less applied) approaches that may circumscribe their ability to find suitable employment after graduation. Moreover, the hiring policy needs to change in many countries, such as Canada, where there are fewer women at entry-level and senior ranks within geography, especially in physical geography as the scientific part of the discipline. The exclusion of women in traditionally male-dominated spheres needs to be addressed and rectified for the ascent of women to occur in scientific geography and in other geosciences as well as science and engineering at large.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.094 | 0.043 |
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".