Mining, the sustainable development goals and impact assessments: a review of governance and local impacts
Bibliographic record
Abstract
Impact assessments have the potential to advance the Sustainable Development Goals (SDGs). This paper argues that the nexus between the SDGs, mining, and impact assessments has received little attention. In particular, research on the governance and local impacts of the mining industry is scant. Using a narrative review approach, this paper explores the processes and practices that might be used to examine the role of impact assessments in addressing the SDGs under three interconnected themes: 1) Impacts on the environment; 2) Governance; and 3) Livelihoods. The review brings together dispersed literature, across various disciplines, and relates to diverse locations in order to provide a comprehensive overview of key debates in relation to the SDGs and impact assessments in mining. Overall, there are eight goals (SDG 1, 2, 4, 7, 9, 10, 11, 17) that are not addressed in the literature reviewed for this paper. Ultimately, this narrative review reveals key themes for future research, including how women’s intersectional identities contribute to their place or position in processes that inform impact assessments and the SDGs, and the need to interrogate what agency, power, scale, and other discursive practices around whose knowledge counts means for both impact assessments and the successful implementation of the SDGs.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".