Navigating sticky floors and glass ceilings: Barriers and opportunities for women's employment in natural resources industries in Canada
Bibliographic record
Abstract
Abstract Women make up almost half the Canadian labour force and more than 50% of post‐secondary students. However, in natural resources (NR) industries (energy, mining, forestry), they represent less than 20% of the workforce, face persistent wage gaps, hold traditionally gendered roles (in sales, administrative and support services) instead of technical or managerial positions, and are persistently absent from leadership roles. Retention of women is also a big challenge in these industries: many tend to leave their jobs within the first five years of employment, and/or after one or more maternity leaves. Women are very poorly represented in leadership positions (as senior executives and board members) despite significant evidence that gender diversity in leadership is good for business. Findings from our study of the status of women in NR employment in Canada produced concrete policy recommendations for recruiting, retaining, and promoting women in energy, mining, and forestry. Although these are intended specifically for Canadian organisations, they may also be relevant for other countries where women are underrepresented in NR industries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 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".