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Record W2770273356 · doi:10.1080/15348458.2017.1366271

Self-esteem and Cultural Identity in Aboriginal Language Immersion Kindergarteners

2017· article· en· W2770273356 on OpenAlexafffundabout
Lindsay Morcom

Bibliographic record

VenueJournal of Language Identity & Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMainstreamSelf-esteemPedagogyPsychologySociologyFrench immersionSelf-conceptCultural identitySocial psychology

Abstract

fetched live from OpenAlex

In gauging the success of Aboriginal language immersion education, the focus is often placed on measuring language acquisition and academic achievement. Although useful, these metrics only tell part of the story; to achieve real school success, it is also vital to develop high personal self-esteem that results in a positive concept of oneself as a learner, and high collective self-esteem, or attitude toward one’s heritage, family, community, and school. This article describes the impact of Anishinaabemowin (Ojibwe) immersion education on the personal and collective self-esteem of kindergarteners, and discusses their concept of ethno-cultural identity, as compared to previously studied cohorts of Inuit learners in immersion and mainstream language schools. The results give important insights into not only the self-esteem of children in this immersion school as part of a measure of its overall success, but also the school experiences of Aboriginal children in different cultural, geographic, and educational contexts.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.023
GPT teacher head0.487
Teacher spread0.464 · 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 designObservational
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

Citations36
Published2017
Admission routes3
Has abstractyes

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