Deepening counter institutions: Property, lands, relations, and the economic future of the Tŝilhqot’in
Bibliographic record
Abstract
The recognition of Aboriginal title by the Supreme Court of Canada in 2014 affirmed the existence and relevance of a Tŝilhqot’in legal order governing the relationship that Tŝilhqot’in people have with their lands, with each other, and with outsiders. The challenge now for the Tŝilhqot’in is to articulate and enact these laws in ways that respond to their modern socio-economic and cultural-ecological needs and goals without betraying their fundamental principles. Complicating this is a dominant narrative which rationalizes First Nations compliance with liberal institutions of British common law, property, and market-based economic growth as requirements for socio-economic improvements and well-being within First Nations communities. This article interrogates some of the logics and fundamental assumptions that underpin the arguments of liberal property rights enthusiasts, questioning their applicability to the values and aspirations of the Tŝilhqot’in people and First Nations broadly. The Tŝilhqot’in, empowered through title, at once resist liberal private property while at the same recognize the need for institutional developments in relation to lands, housing, and ‘ownership’. This indicates a need for new legal conceptualizations of property that are more comprehensively rooted in, and reflective of, Indigenous laws and land relations.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.049 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".