Used and Abused: Negotiated Agreements
Bibliographic record
Abstract
Aboriginal groups and mining proponents are taking a transactional approach through negotiated agreements to work in partnership. This paper examines how bilateral agreements are used to maximize legal certainty and work cooperatively through the regulatory mine approval process. Anecdotal evidence is presented on how the Crown is benefiting from negotiated agreements to lessen their fiduciary duty towards Aboriginal peoples vis-à-vis third parties, and how this ‘weighing-in ’ strains the original spirit and intent of what the agreement set out to do. This paper draws on a British Columbia, Canada, case study to consider how the Crown is using and perhaps even abusing negotiated agreements. Few would disagree that the last quarter century has witnessed a fundamental change in how Aboriginal groups and the mineral industry in Canada operate. Within this transformative extractive climate, a transactional approach has been taken up and is most evident in Negotiated Agreements (NAs), signed between mining proponents and Aboriginal groups.
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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 | 0.033 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.021 | 0.050 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".