Ráhskan Oká:ra: Transcriptions and translations of a Rotinonhsión:ni narrative
Bibliographic record
Abstract
Ce mémoire présente une étude de cas sur la revitalisation des langues autochtones,centrée sur la langue Kanien’kéha (Mohawk). Ancrée dans une approche communautaire, larecherche porte sur la transcription et la traduction d’un récit oral enregistré, partagé par unlocuteur kanien’kéha de langue maternelle. Ce récit, une ancienne histoire Rotinonhsión:ni(Iroquoienne), a servi de point d’ancrage riche pour l’exploration linguistique et culturelle. Grâceau processus de transcription et de traduction, j’ai collaboré avec divers membres de macommunauté, chacun apportant des perspectives uniques sur la langue, les conventions narrativeset les significations culturelles inscrites dans l’histoire. Cette expérience immersive a nonseulement renforcé mes compétences linguistiques, mais aussi approfondi ma compréhensionculturelle et mon lien avec les savoirs communautaires. Ce mémoire réfléchit à ce processuscollaboratif et itératif, à la fois comme méthode et comme voie d’apprentissage linguistique, derenouveau culturel et de réappropriation des formes narratives traditionnelles desKanien’kehá:ka
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".