Collective Self-Determination, Territory and the Wet'suwet’en: What Justifies the Political Authority of Historic Indigenous Governments over Land and People?
Bibliographic record
Abstract
Abstract This article examines the Wet'suwet’en people's struggle for territorial control over their traditional homeland from the normative perspective of collective self-determination. I focus on two interlocking philosophical questions that arise in examination of the case: the justification for a group's right to control territory and the justification for the right of political institutions and officials within those institutions to make and enforce law for the occupants of the territory. I argue that, pursuant to the collective self-determination theory of territorial rights, the legitimate representatives of the Wet'suwet’en people must reflect the people's shared will. After describing the traditional governance system of the Wet'suwet’en people, I argue that there is nothing in principle preventing the hereditary chiefs from reflecting the shared will of the Wet'suwet’en people (as I argue electoral democracy is not always necessary for collective self-determination). I illustrate several reasons why hereditary leaders could reflect the shared will of Wet'suwet’en people better than alternative political authorities, while demonstrating that the political authority of any Wet'suwet’en governance system depends upon the actual endorsement of Wet'suwet’en people themselves.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".