Bibliographic record
Abstract
In the Kootenay region of British Columbia (BC), mining has historically been a male dominated sector. Natural resource dependent communities, such as those found in the Kootenay region, have traditionally lacked employment opportunities for women. In addition, women choosing to pursue careers in the mining sector can face numerous challenges. As part of the larger Regional Workforce Development in Rural BC project, this research focuses on ways to build a more gender diverse mining workforce in the Kootenay region. Building connections to planning theory and practice in the region, the paper investigates the opportunities available to women in rural resource communities, and the challenges they face regarding employment in the mining sector. Results show that in the Kootenay region there may be a shift occurring in mining company culture. More progressive policies and respectful workplace culture, combined with gender-oriented planning initiatives, appear to create a more gender inclusive mining sector. The results also show significant challenges associated with social infrastructure and supports, specifically a lack of child care spots, high rental and real estate prices, and the lack of opportunities for further education for women who want to enter the Kootenay mining workforce.
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.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.043 | 0.006 |
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".