Bibliographic record
Abstract
Resume L'elaboration de ressources linguistiques qui aident a la preservation des langues amerindiennes offrent bien des defis. Bien qu'on compile des dictionnaires, les locuteurs et locutrices sont souvent bien en peine de les utiliser. La standardisation recente de l'orthographe, la predominance de la langue orale et une grande variation dialectale font que trop souvent, les gens ne trouvent pas les mots qu'ils cherchent car ils les ecrivent de travers ou juste differemment. Nous montrons ici comment nous avons construit un moteur de recherche pour le dictionnaire cri de la Baie James sur le web (www.eastcree.org) qui permet les fautes d'orthographe ou les orthographes creatives, et comment nous l'avons ensuite incorpore dans un moteur plus complexe pour la recherche de verbes a partir de formes verbales flechies Nous montrons aussi comment nous avons adapte ces outils a une langue voisine, l'innu (ou le montagnais). Notre solution est de combiner deux approches computationnelles a la correction de l'orthographe (en mesurant la difference entre le mot entre et les mots du dictionnaire, et en appareillant la phonetique), et de les adapter aux langues algonquiennes a partir de connaissances linguistiques. Ce moteur pourrait servir de modele et etre adapte pour d' autres langues algonquiennes ou minoritaires.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".