Self-Determination in mine site transitions and mine closure governance across Indigenous nations
Bibliographic record
Abstract
This introductory article provides a synthesis of and a conceptual framing for, the five contributing articles in the Special Section "Self-determination in mine closure and mine site transition across nations". These articles explore the intersections of mineral extraction, environmental legacies, and Indigenous post-mining futures in the context of mine site transitions. The articles provide a series of Indigenous-authored and collaborative contributions offering a deeper exploration of community perspectives, engagements and governance practices at extractive sites in Australia and Canada. Written from diverse ecological, social and political contexts, taken together the articles elevate Indigenous voices and experiences in mine closure governance. This addresses the significant gap in the literature on the social aspects of mine closure, which is particularly glaring in relation to Indigenous peoples' rights and interests. The mines that are the focus of consideration in this Special Section cover various commodities including gold, diamonds, nickel, zinc, lead, silver and copper. Many of them were, and in some cases still are, long-lived mines. Though they all have particular histories and contexts, all the Indigenous commentators reflect on mining as an expression of the continuity of settler colonialism and environmental injustice.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".