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Saving Culture Through Language: A Hermeneutic Phenomenological Study of Ojibwe Language Immersion Educator Experience

2013· article· en· W4400610474 on OpenAlexvenueno aff
Brian Donald McInnes

Bibliographic record

VenueJournal of Applied Hermeneutics · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPedagogyLived experienceSociologyExcellencePsychologyLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

With the near extinction of many tribal languages at the present, language immersion education offers considerable promise for the restoration of Ojibwe culture and identity. Through a series of structured interviews and longitudinal text-based dialogue with the researcher, eight educators from three school-based programs described the lived practice of working with culture in language immersion education. The study principally revealed how the lived experience of Ojibwe language immersion educators is important, challenging, and rewarding. The dynamic synergy of culture, language, and content that happens in Ojibwe language immersion requires competent and creative educators who are knowledgeable about language, cultural traditions, and teaching practice. The study concludes by noting how the lived experience of immersion school practitioners as cultural workers is marked not only by excellence in practice and professionalism, but by dedication, responsibility, and hope for the future of the culture, language and tribal nation itself.

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)0.006
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0180.020
Scholarly communication0.0080.006
Open science0.0020.008
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.029
GPT teacher head0.286
Teacher spread0.258 · 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 designQualitative
Domainnot available
GenreEmpirical

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
Published2013
Admission routes1
Has abstractyes

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