Navigating sticky floors and glass ceilings: Barriers and opportunities for women's employment in natural resources industries in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it