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Record W2518910238 · doi:10.4000/droitcultures.3909

Anishinaabemowin Oodenang. Préservation et revitalisation d’une langue citadine autochtone

2016· article· fr· W2518910238 on OpenAlexaffabout
Brock Pitawanakwat

Bibliographic record

VenueDroit et Cultures · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversity of WinnipegUniversity of Sudbury
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Le présent article explore les motivations, méthodes et la concertation d’un peuple autochtone, celui des Anishinaabeg (également connus sous les dénominations d’Ojibway, Saulteaux, ou Chippewa), dans leur effort de préserver et revitaliser leur langue ancestrale dans des zones urbaine du Canada. Pourquoi ces citadins ont-ils choisi cette démarche de conservation et de revitalisation de l’Anishinaabemowin (la langue Anishinaabeg) dans un contexte qui exerce une énorme pression d’assimilation sur les non anglophones ? À quelles méthodes les Anishinaabeg des villes ont-ils recours pour continuer à parler leur langue ? À travers son expérience et ses entretiens avec d’autres activistes de la langue Anishinaabeg, l’auteur se livre à une enquête sur les motivations des Anishinaabeg en milieu urbain et sur les efforts pédagogiques pour redonner un souffle à l’Anishinaabemowin par le biais de réseaux des activistes de la langue Anishinaabeg, enseignants et étudiants. Enfin, cet article se prend à imaginer le futur de la langue Anishinaabeg préservée et revitalisée à partir de l’expérience des Kanaka Maoli (autochtones Hawaiiens), du recensement international fondé par la Déclaration de l’ONU sur les Peuples Autochtones et sur les « Appels à l’Action » en faveur de la revitalisation des langues autochtones en milieu urbain.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.301
Threshold uncertainty score0.599

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0090.004
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.069
GPT teacher head0.446
Teacher spread0.376 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2016
Admission routes2
Has abstractyes

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