Bibliographic record
Abstract
In settler societies, tribal self-governance creates a legal distinction between indigeneity (defined by settler governments) and tribal membership (defined by tribes). Many legally indigenous persons are not tribal members, and some tribal members are not legally indigenous. This book considers the membership rules included in the constitutions and membership codes of nearly 750 recognized tribes in Canada, New Zealand, Australia, and the United States. It addresses the first-order question of tribal constitutionalism: who are the members of tribes, and how are they chosen? The question is of practical and theoretical import. A large proportion of indigenous peoples in each state are not enrolled in a recognized tribe, and the majority of indigenous peoples do not live near their tribal territories. The book's empirical study challenges many of the assumptions used to model tribalism in theories of cultural pluralism, especially those that depict tribes as distinctively insular, ascriptive, and territorially-confined. The book shows that while they are descent-based groups, tribes also self-constitute relationally, by enrolling non-descendants in accordance with cultural and social criteria, and by recruiting from other indigenous communities. The book draws on tribal law and practice, political theory, legal doctrine, policy, and demographic data to critically assess the strategies used by tribes and states to manage the jurisdictional and ideological challenges of tribal membership 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".