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Record W4399715627 · doi:10.1080/19313152.2024.2367812

Critical teacher education for equitable learning in multilingual classrooms: a possible way forward

2024· article· en· W4399715627 on OpenAlexaff
Laxmi Prasad Ojha, Jennifer Burton, Peter I. De Costa

Bibliographic record

VenueInternational Multilingual Research Journal · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsConcordia University
Fundersnot available
KeywordsCritical consciousnessTeacher educationPedagogyEquity (law)Educational equitySociologyMathematics educationBilingual educationPsychologyPolitical science

Abstract

fetched live from OpenAlex

Addressing the ongoing calls to reform teacher education to prepare future teachers to serve students from diverse backgrounds, this introduction reviews recent developments in teacher education to situate our thematic issue on critical teacher education for equitable learning in multilingual classrooms. The five empirical papers and two commentaries included in this issue focus on the connection between language, power, and critical consciousness to address equity concerns in teacher education as it pertains to supporting multilingual learners, asking: how do teacher education programs prepare teachers to work with diverse students in schools where there exists a long-standing history of marginalization and discrimination based on racial, economic, social backgrounds? In this introductory paper, we discuss the contributions of the papers included in the issue and share a vision for new ways for reimagining teacher education for multilingual learners.

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)0.013
metaresearch head score (Gemma)0.017
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: none
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0060.016
Scholarly communication0.0200.024
Open science0.0020.009
Research integrity0.0120.012
Insufficient payload (model declined to judge)0.0070.001

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 head0.177
GPT teacher head0.600
Teacher spread0.423 · 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

Citations24
Published2024
Admission routes1
Has abstractyes

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