Gender and sustainability reporting – Critical analysis of gender approaches in mining
Bibliographic record
Abstract
The mining sector remains economically significant across the globe. With a number of growing sustainability concerns (from environmental waste and pollution to social and ethical considerations), most major mining companies have highlighted sustainability concerns and social license to operate as corporate priorities. A number of case studies have also identified serious gender concerns, including disproportionate negative effects for women (especially for Indigenous, racialized, or women working in artisanal mining sectors). Here, we analyze gender dimensions of sustainability reporting to understand how large mining companies monitor and report on these concerns. This provides an understanding of what gender concerns are acknowledged and reported on by industry, as well as those that are not included. We selected a subset of large-scale mining companies that are considered likely to foreground commitments to these issues (members of the International Council on Mining and Metals, ICMM), and analyzed their recent sustainability reports to understand how gender and related intersectional issues are acknowledged, framed, and addressed in voluntary reporting by companies. Among other findings, we highlight that while some company reports highlight gender issues with respect to female employees, or maintaining community relations—this is often narrowly focused on women, rather than a broader gender or intersectional perspective. As well, we are able to identify a range of issues where specific effects for women are addressed, as well as a suite of concerns for which a broader gender and intersectional perspective is needed.
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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 | 0.080 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".