Inalienable Properties : The Political Economy of Indigenous Land Reform
Bibliographic record
Abstract
As many Indigenous communities return to self-governance and self-determination, they are taking their own approaches to property rights and community development. Why did the Nisga’a Nation introduce property rights that can be traded in the market? And how have communities such as the Membertou First Nation sustained control over their lands in the face of economic pressures for saleable rights? This book explores the contrasting approaches to land rights illustrated by four Indigenous communities in Canada – the Westbank, Membertou, Nisga’a, and James Bay Cree Nations. Jamie Baxter traces how local leaders set the course of land rights and development in their communities during formative periods of legal and economic upheaval. Drawing on new research about institutional change in organizational settings such as business firms and labour unions, Baxter uses game theory to explore how community leaders have sustained inalienable land rights without turning to either persuasion or coercive force – the two levers of power normally associated with political leadership. Inalienable Properties challenges the view liberalized land markets are the inevitable result of legal and economic change. It shows how inalienability can result from intentional choices and is linked to structures of decision-making that have long-lasting consequences for communities.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".