Indigenous – Corporate Private Governance and Legitimacy: Lessons Learned from Impact and Benefit Agreements
Bibliographic record
Abstract
This paper argues that impact and benefit agreements (IBAs) between Indigenous groups and resource companies are properly understood as a form of private governance. Viewing IBAs through a private governance lens generates important insights for both governance scholars and for scholars interested the structuring of Indigenous-corporate relations in the context of resource development. In order to develop this argument, we present a case study of a specific arrangement between a Canadian First Nation and a multi-national mining company with a particular focus on the governance elements of the contract and the implications for legitimacy that arise from the arrangement. Our central claim is that IBAs, as a form of private governance, require a theory of legitimacy that goes beyond contractual consent, but must account for both procedural and substantive legitimacy demands. We then identify the key lessons that can be taken for indigenous law and governance scholars and private governance scholars from our analysis.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.061 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".