The role of the Tłıchǫ Comprehensive Agreement in shaping the relationship between the Tłıchǫ and the mining industry in the Mackenzie Valley, Northwest Territories (NWT), Canada
Bibliographic record
Abstract
The mining sector has played a dominant role in the expansion of the economy of the Northwest Territories (NWT) and the promotion of the development of infrastructure to fuel growth of communities. Comprehensive Land Claims Agreements (or modern treaties) are constitutionally protected agreements defining the relationship between Aboriginal signatories, the Government of Canada and in some cases a province or territory. Despite the importance of these agreements in natural resource-rich parts of northern Canada, little is known about the way they shape the relationship between Aboriginal signatories and mining companies. Drawing on the analysis of government documents, a critical review of the academic literature and some semi-structured interviews, this case study examines the ways in which the Tłı̨chǫ Comprehensive Agreement has helped shape the relationship between the Tłı̨chǫ people and the diamond mining industry in the Northwest Territories. In the normally contested space of mineral extraction on Indigenous lands, this chapter examines how the two interrelated processes of the delegation of governance duties by the Canadian government to industry through impact benefit agreements and the concomitant evolution of corporate social responsibilities have impacted the governance power dynamics between the Tłı̨chǫ and the diamond mining industry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".