Gender and sustainability reporting – Critical analysis of gender approaches in mining
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
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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