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Record W3202929765 · doi:10.53967/cje-rce.v44i3.4461

Strategies for Teacher Education Programs to Support Indigenous Teacher Employment and Retention in Schools

2021· article· en· W3202929765 on OpenAlexaffvenueabout
Danielle Tessaro, Laura Landertinger, Jean‐Paul Restoule

Bibliographic record

VenueCanadian Journal of Education / Revue canadienne de l éducation · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsPracticumIndigenousTeacher educationIndigenous educationPedagogyPolitical scienceSociologyMedical educationPsychologyMedicine

Abstract

fetched live from OpenAlex

This article seeks to contribute to the knowledge base regarding efforts to increase the supply of employed Indigenous teachers. In addition to supporting the learning and well-being of Indigenous students, increasing Indigenous teachers is critical for remote Indigenous communities with chronically understaffed schools. This study was conducted as a scoping review of 50 Teacher Education Programs (TEPs) across Canada, Australia, New Zealand, and the United States that have enacted efforts to increase Indigenous teachers. The study found a range of effective strategies, and this article will depict three strategies that can be enacted by TEPs to support Indigenous teacher graduates as they transition to employment. The strategies are: (1) creating employment opportunities, (2) identifying community needs and collaborating over practicum placements, and (3) providing ongoing support. The article concludes with a call for collaboration, funding, and data collection for the continued evaluation and improvement of strategies to increase Indigenous teachers. Keywords: teacher retention, teacher support, teacher employment, Indigenous teacher education, job transition, Indigenous teachers, Indigenous education, teacher education programs

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.686
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.082
GPT teacher head0.360
Teacher spread0.278 · 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 teacher head, 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

Citations6
Published2021
Admission routes3
Has abstractyes

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