“Learning to Be Indian”: Historical Narratives and the “Choice” of a Cultural Identity
Bibliographic record
Abstract
Résumé Cet article analyse le besoin qu'éprouvent certaines personnes de se «reconnecter», ou renforcer lews liens, à une culture dans laquelle elles n'ont jamais été enchâssées, eu égard aux critères auxquels se mesure généralement un tel enchâssement (langue, valeurs, croyances, modes de vie, coutumes religieuses, etc.). Situant les résultats de son analyse par rapport à la pensée de Kymlicka et autres, il fait valoir que des facteurs tels que les liens de parenté, l'identification à autrui et l'expérience du racisme déterminent largement quel est le groupe culturel que les individus parviennent à considérer comme le leur.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.037 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".