Bibliographic record
Abstract
Abstract Non-Native citizens of the United States and Canada often argue that it is unfair for Native peoples to have distinctive political and property rights within countries purportedly dedicated to equal treatment. Yet Native nations in the United States and Canada have long made claims for a more contextually rich sense of fairness, and their legal and political successes in these efforts—difficult, uneven, and partial as they have been—have allowed them to continue to exist into the present. Their fairness arguments have thus found traction even in the face of longstanding political animosity. Situated within debates on ideal and non-ideal theory, this book seeks to show the normative force of such arguments within a contextually rich theory of political fairness for Native peoples in the United States and Canada. Structured to be accessible to political theorists and their students with little background in Native politics, the book argues that this broader conception of fairness applies in relation to political sovereignty, ownership rights, cultural choices, and—uncomfortably—racially inflected standards of tribal membership. Seeking to outline parameters for potential future political orders, it argues that such a contextually rich standard of fairness is likely to be required long into the future as well, given the unavoidably variegated texture of human social order.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".