What is the experience with governance models that manage and engage diverse stakeholders through a closure transition?
Bibliographic record
Abstract
Traditionally understood in technical, environmental and (to a lesser extent) socio-economic terms, mine closure and transition is increasingly recognized as a significant governance challenge. Governance, in this context, refers not merely to the legal aspects of mine reclamation or closure regulation but rather the broader suite of actors, institutions, processes, methods, rules and practices that guide and oversee mine site transitions. Governance structures, interactions and practices are shaped by power relations as well as reflecting embedded norms and values. Since the 1980s, mine closure governance has expanded from a preoccupation by industry and governments with hazard mitigation, environmental reclamation and, in some cases, economic and social ‘adjustment,’ to encompass a wider set of social, economic and environmental aspects of closure (Kendall 1992; Laurence 2006). These issues may affect workers, local and regional development agencies, Indigenous rightsholders, fenceline communities and environmental advocates, among others (Bainton and Holcombe 2018; Everingham et al. 2020). This broad range of actors and issues, in turn, has generated reactions and responses from individual companies, industry associations and governments at all levels seeking to mitigate closure and transition impacts (Morrison-Saunders et al. 2016; Owen and Kemp 2018; Hodge and Brehaut 2023).
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".