The contribution of impact and benefit agreements to the regulation of mining projects: lessons from the raglan agreement in northern Quebec
Bibliographic record
Abstract
Since the early 1990s, the Canadian mining industry has been increasingly defined by the signing of Impact and Benefit Agreements (IBAs) between mining companies and Aboriginal peoples. While these agreements are intended to complement the legal system, they also serve to establish the legitimacy of mining projects and to encourage the harmonious integration of these projects into their social and environmental contexts. Beyond their practical implications, what are the effects of such agreements on the ability of Aboriginal communities to access the institutional space where the authorization of mining projects and the conditions of their implementation are decided? Can we view IBAs as a preliminary step towards the operationalization of the principle of Free, Prior and Informed Consent? This paper aims to explore the potential of IBAs as a sustainable solution to the legitimacy issues that result from the implementation of the formal regulatory framework governing mining activities in this jurisdiction. We highlight what constraints and opportunities the James Bay and Northern Quebec Agreement (JBNQA) engenders for local communities and draw attention to the ways in which the negotiation of the Raglan IBA and the environmental evaluation processes act upon each other. We find that certain structural problems - and problems of legitimacy – related to mining in Northern Quebec cannot be solved by the sole signature of an IBA, which suggests the need for caution when considering such agreements as a panacea. The Raglan case demonstrates that IBAs, like other modes of regulation relying on the direct interface between companies and communities, would benefit from a stricter framework and more explicit ties with official regulation channels.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".