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Record W2994680438 · doi:10.21810/sfuer.v12i3.1013

Critical Content for Teacher Education

2019· article· en· W2994680438 on OpenAlexfundvenueaboutno aff
Koichi Haseyama, Fumito Takahashi

Bibliographic record

VenueSFU Educational Review · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
FundersSimon Fraser University
KeywordsMulticulturalismPedagogyLegitimacyTeacher educationSociologyContent analysisMulticultural educationAutismCritical theoryPsychologyMedical educationSocial sciencePolitical scienceMedicineDevelopmental psychologyLaw

Abstract

fetched live from OpenAlex

This autoethnographic study explores our experiences as postsecondary researcher-educators with a particular focus on our team teaching experience in a teacher education course at Western Vancouver University, located in Metro Vancouver, British Columbia (BC), Canada. We have introduced a case study of a Japanese temporary resident family with a toddler to our teacher candidates of BC. This case study was based on an interdisciplinary analytical lens: educational sociolinguistics and clinical psychology, which examined the case of the child having been diagnosed with mild autism in the BC’s medical system. The authors introduced this multicultural pedagogical content in higher education in order to cultivate critically internationalized analytical lenses of the teacher candidates. Our critical analysis of this clinical case suggested more than what the medical diagnosis had claimed. This contribution aims to 1) problematize the lack of societal awareness and the legitimacy of such scholarly inquiries, and 2) explore what impacts such critical multicultural contents may bring to teacher education in our multilingual and multicultural society.

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)0.008
metaresearch head score (Gemma)0.027
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0040.011
Scholarly communication0.0080.006
Open science0.0010.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0100.002

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 head0.107
GPT teacher head0.380
Teacher spread0.273 · 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
Published2019
Admission routes3
Has abstractyes

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