Abandoned mine clusters and their intersection with Indigenous peoples’ land rights in Australia
Bibliographic record
Abstract
Empirical research on the intersection of Indigenous peoples and abandoned mines has primarily focused on the impacts of individual, large-scale mines in the settler states of Australia, Canada and the United States. In contrast, research on the extent and effects of dense clusters of relatively small, abandoned mines has been largely overlooked. Australia has 50,000+ abandoned mines and their overlap with Indigenous peoples' legally recognised rights to land has not been mapped or quantified. This study presents a novel methodology to map and quantify this intersection using the state of Queensland as a case study. Through spatial data and document analysis, we find that 54.8 % of Queensland's abandoned mines are located where Indigenous peoples have rights to land and we identify five dense clusters that warrant further examination. Our findings provide an empirical basis for regulators, mining companies, land use planners and Indigenous communities to address significant policy and practice shortcomings. Recognising abandoned mines as a pressing governance challenge-not merely a historical remnant-is a crucial step towards advancing environmental sustainability, Indigenous land justice, and equitable land management.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| 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".