Time for an Outcome Evaluation? The Experience of Indigenous Communities with Mining Benefit Sharing Agreements
Bibliographic record
Abstract
Much has been written about the promise and potential of mining to deliver or catalyse development opportunities for host communities. Where mining projects affect Indigenous communities, leading practice often encapsulates these aspirations in benefit sharing agreements signed between mining companies and communities. In Australia and Canada these agreements often take the form of Indigenous Land Use Agreements and Impact Benefit Agreements, respectively. With more than 60 per cent of mining in Australia alone occurring in proximity to Indigenous communities, it is clear that the future of mining is dependent on effective delivery against the expectations and aspirations of Indigenous communities that form the basis of these agreements. In Australia and beyond, mining occurring on the lands of Indigenous peoples has become the source of increasing calls for a ‘new development model’. However, in order to articulate a call for a new model, the success of the existing model needs to be adequately evaluated, and while this has been occurring during the operational period, it is also necessary when a mine (and an agreement) comes to an end. By reviewing the existing literature, and specifically that encapsulating practitioner experience, this chapter highlights the gap in research evaluating the effectiveness of the existing benefit sharing model for Indigenous communities, as judged by those communities, at the time of mine closure. To some extent this gap is due to the limited number of mines that have closed where benefit sharing agreements had been in place; opportunities for such research have, however, existed and now need to be pursued. Development projects and policies are typically subject to an outcome evaluation process, and given the criticality of the relationship between Indigenous peoples and the extractive sector the time is ripe to evaluate existing approaches in order to inform the benefit sharing models of the future.
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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 | 0.323 | 0.356 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".