Anticipating the unforeseeable? ESG risk management in mining companies
Bibliographic record
Abstract
The aim of this article is to investigate the foreseeability of environmental, social and governance (ESG) risks in the mining sector by analyzing the impacts anticipated when mining projects were first submitted to the authorities, and crises or critical incidents observed ex-post at mining sites. An in-depth analysis of 57 critical sustainability incidents that occurred at 19 different Canadian mining sites, and of the way in which companies and stakeholders anticipated or failed to anticipate them in prior risk analyses, enables us to map the main impacts of this industry and to highlight the uneven ability of companies and stakeholders to effectively anticipate them. The results obtained were analyzed through an integrative model with four main configurations of risk foreseeability: high-visibility risks (good anticipation by both companies and stakeholders), stakeholder red flags (risks identified by stakeholders only), corporate foresight (risks identified by companies only) and black swans (risks neglected by both companies and stakeholders). This article makes substantial contributions to the literature on the foreseeability of ESG risks, the uncertain ways that polluting companies integrate such risks into their planning, and the management of critical sustainability incidents. Practical implications and avenues for future research are also developed. • Risk can be anticipated by companies or stakeholders. • Mining companies de not systematically integrate ESG risks into their planning. • Critical incidents are multidimensional and can cover many ESG risks. • Stakeholders are better than companies to anticipate ESG risks.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".