Bibliographic record
Abstract
First Nations' emerging choices over their property institutions, however, are considerably more complex than perennial debates about private-individual versus communal rights would tend to suggest. [...]the information-based perspective suggests: (i) that First Nations should prioritize simple, bright line property rules that eschew the uncertainties of community-based interpretation and context; and (ii) that First Nations should work to harmonize their local property systems with a uniform set of norms familiar to Anglo-Canadian common law.2 My purpose in this Essay is to question these two basic prescriptions and offer some alternative ways of thinking about property, information, and institutional design. In Part II, I describe a second implication of the information-based approach - namely, the idea that communities should seek to reduce local variation in their property regimes. Because local divergence from widely used common law property norms is thought raise the costs of transactions across community boundaries, recent research suggests that there may be substantial incentives for communities to move toward harmonization or convergence, at least over the long run. For several reasons, investors will demand secure property - i.e., well defined and broadly agree upon norms with predictable and enforceable consequences.4 Using uncertain legal standards to delineate property rights appears to cut against this accepted logic by undermining real and perceived security and discouraging economic investment. [...]because uncer - tainty around property can effectively delegate important legal and political decisions to nonmajoritarian institutions and third-party decisionmakers from outside local communities, it seems that this strategy might also be unattractive from the perspective of strong, autonomous First Nations governance.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".