Sexual violence and extraction: Interrogating mining executive discourses of corporate social responsibility, violence, and impunity
Bibliographic record
Abstract
Gender-based and sexual violence permeates resource extraction. This violence operates in many forms and spheres, both public and private. Focusing on a case of overt, public violence, we ask, what is productive about gender-based and sexual violence for mining corporations? In the context of commitments to social responsibility and the gender impacts of mining, what can explain corporate engagement with acts of extreme violence that publicly undermine these commitments? We respond by exploring the case of the 2007 attack on Maya Q’eqchi’ women near the Fénix nickel mine in Guatemala. Following an attack allegedly involving gang rape by public-private armed forces, eleven survivors mobilized to demand justice in the landmark Caal v. Hudbay legal case in Canada. Our analysis offers a reading of the internal communications of mining executives and their affiliates, which were released through the case. Bringing these data in conversation with critical theories of race, gender and extraction, we argue that the mining company benefitted not only from the gendered suppression and discipline of resistance, but also from the reinforcement of a racialized view of Guatemala as violent—a stereotype that allows Canadian corporate executives to continue to project their goodness, regardless of the substance of their actions.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.038 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".