The long reach of frontier justice: Canadian land claims ‘negotiation’ strategies as human rights violations
Bibliographic record
Abstract
In this article, we argue that the Canadian land claims process is the product of a series of policies and laws directed at indigenous peoples which both denies them consent over the relinquishing of their lands, and is characterised by a lack of attention to the rights vested in indigenous peoples from colonial precedents. As a result, the contemporary Canadian land claims process does not measure up to the United Nations Declaration on Indigenous Peoples (UNDRIP) and other international human rights protocols. It does not meet even rudimentary standards in regard to providing informed consent, requiring indigenous peoples to extinguish their ownership of their lands, dividing indigenous peoples into configurations that are artificial and diminishing their negotiating power, and creating invidiously asymmetric responsibilities between the state and the indigenous party. Our analysis will principally be based on a reading of the Innu Nation Tshash Petapen (New Dawn) land claims agreement and the social and political contexts in which it is situated. We conclude from our readings that expedients used in the past to obtain indigenous peoples' lands and to circumvent the colonial laws governing relationships with indigenous peoples are still evident today in Canada. They survive as a kind of victor's justice worthy of the frontier.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.056 | 0.050 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".