35a - Supporting women in mining: diversity and inclusion
Bibliographic record
Abstract
Abstract Traditionally, the mining sector has not been viewed as a leader in effectively managing diversity and inclusion. While numbers have improved, women continue to be underrepresented at every level within mining companies. Currently, the mining sector is committed to increasing diversity and improving inclusion. Globally, mining has set gender-based targets for the sector. They are actively investing in the success of women in mining and jurisdictions and organizations are tracking and reporting on diversity and inclusion performance. Mining organizations are engaged in improving inclusion-related efforts that promote the sense of belonging for workers. Why is diversity and inclusion important to mining? Diversity and inclusion promote creativity and strategic resilience. This is important as mining companies manage the complex challenges associated with community, environmental, social (including health and safety), and technical aspects of their industry. With increasing numbers of women in surface and underground mining and processing operations, a strategy is needed to better support these workers including their health, safety, and well-being, including reproductive health. This presentation will review the history of women in mining, challenges for women to access decent work in mining, suggestions that support recruiting and retaining women in the sector, and the path forward towards gender equality in mining. The importance of advancing equal employment opportunity and human rights in the workplace will be highlighted, along with the need for additional research to support women’s reproductive health and mitigate the risk of adverse reproductive outcomes. The next presentation will cover in more depth this latter topic.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".