Where Are We With Gender Parity in Academia and Professional Societies? A Multinational Look at Women in Soil Science
Bibliographic record
Abstract
ABSTRACT Issues of diversity, equity and inclusion (DEI), including gender equity, have gained increasing recognition at the beginning of the 21st century. As an academic discipline, soil science has been late in addressing gender equity, but several peer‐reviewed studies have been published in the last 5 years. This study investigated gender equity in the soil science university faculty/academic staff (f/as) and soil professional societies in Canada, Egypt, Mexico, Nigeria, the United Kingdom and the United States of America (USA) using data publicly available on the Internet and anonymized data from soil science professional societies. We found that women still lagged behind men among our soil science f/as by considerable margins (69.2% men vs. 30.8% women for pooled data for all six countries in this study), and that the percentage of women in soil science f/as positions are similar to those for f/as women in many other scientific fields. There are differences in gender equity by soil science subdiscipline that vary by country. In countries where data are available to make comparisons, over the last 6–8 years, the gender gap has closed to some degree, both overall and for sub‐disciplines. Women also often hold leadership positions in numbers that are lower than their representation among the f/as and membership in professional societies. In addition, women are recognised with awards such as society fellowship in numbers lower than their overall representation would suggest. This study concludes that progress has been made on several fronts in the last 6–8 years, but there is still much work to be done to achieve gender equity in soil science academia and professional societies. It is recommended that soil science societies collect, analyse and compare data on gender in the discipline so that progress can be evidenced, tracked and encouraged.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".