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Record W2806618343 · doi:10.5296/jse.v8i2.13154

To Be or Not to Be Decolonized: A Medicine Wheel Healing Education Model

2018· article· en· W2806618343 on OpenAlexafffund
Theresa A. Papp

Bibliographic record

VenueJournal of Studies in Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Saskatchewan
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPrideGraduation (instrument)MainstreamAttendanceCurriculumExperiential learningPedagogySociologyPsychologyPolitical scienceEngineeringLaw

Abstract

fetched live from OpenAlex

Colonization has created a sea of troubles for Aboriginal learners through Eurocentric practices and principles that have marginalized learners resulting in lower educational attainment levels. The best reconciliation approach would be to decolonize classrooms so non-traditional learners who fail in mainstream classrooms can excel. This qualitative case study of a high school, mainly consisting of Aboriginal students, presents a medicine wheel healing education model that has decolonized and indigenized the classrooms resulting in improved educational outcomes, increased credit completion, attendance, and graduation rates. More important, students felt pride and improved self-esteem to be Aboriginal. The teaching style emulated traditional Aboriginal teaching by being hands-on, experiential, wholistic, personal, cooperative, flexible, and self-paced while incorporating Aboriginal culture and knowledge into the curriculum.

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)0.002
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.011
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.106
GPT teacher head0.477
Teacher spread0.372 · 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 designTheoretical or conceptual
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

Citations1
Published2018
Admission routes2
Has abstractyes

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