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Record W4388850323 · doi:10.1080/09658416.2023.2280030

Be(com)ing multilingual listeners: preparing (monolingual) teacher candidates to work with multilingual learners in mainstream classrooms

2023· article· en· W4388850323 on OpenAlexaff
Gail Prasad, Esther Bettney Heidt

Bibliographic record

VenueLanguage Awareness · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsYork University
Fundersnot available
KeywordsMainstreamPsychologyMultilingualismPedagogyLanguage acquisitionCertificationSociologyLinguisticsMathematics educationPolitical science

Abstract

fetched live from OpenAlex

Classrooms across the United States today often include students from multiple different cultural and linguistic backgrounds. The teaching force, by contrast, has remained predominantly White and Anglophone with little experience learning additional languages (Athanases & Wong, Citation2018; Deroo & Ponzio, Citation2023; Pettit, Citation2011). When teachers themselves have limited experience with linguistic diversity, how can teacher ­educators raise teacher candidates’ (TCs) critical multilingual language awareness (CMLA) over the limited duration of the teacher certification process? This article analyzes the implementation of a CMLA project which engaged secondary TCs in becoming language learners themselves to reflect on the experience of being an early language learner. We collected written reflections from 49 TCs about their language learning experiences over 30 h and drew on the five domains of CMLA (Power, Cognitive, Affective, Social and Performance) (Prasad, Citation2022) to code the data set. We focus on the domain of Power to examine how learning a new language even for a short time can engage TCs practically in attending not only to mechanics of teaching and learning with multilingual students but also more critically to recognize the systemic power relations among languages and language users in schools.RÉSUMÉ Aujourd’hui, les salles de classe à travers les États-Unis comprennent souvent des élèves issus de multiples origines culturelles et linguistiques différentes. Le corps enseignant, en revanche, reste majoritairement blanc et anglophone, avec peu d‘expérience dans l‘apprentissage de langues supplémentaires (Athanases & Wong, Citation2018; Deroo & Ponzio, Citation2023; Pettit, Citation2011). Lorsque les enseignants ont eux-mêmes une expérience limitée de la diversité linguistique, comment les formateurs d‘enseignants peuvent-ils élever l’éveil aux langues critiques (CMLA en anglais) des stagiaires pendant la durée limitée du processus de certification des enseignants? Cet article analyse la mise en œuvre d‘un projet de CMLA dans le cadre duquel des stagiaires du secondaire ont été amenés à devenir eux-mêmes des apprenants de langues afin de réfléchir à leur expérience d‘apprenant précoce de langues. Nous avons recueilli les réflexions écrites de 49 stagiaires sur leurs expériences d‘apprentissage des langues pendant 30 heures et nous nous sommes appuyés sur les cinq domaines du CMLA (pouvoir, cognitif, affectif, social et performance) (Prasad, Citation2022) pour coder l‘ensemble des données. Nous nous concentrons sur le domaine du pouvoir pour examiner comment l‘apprentissage d‘une nouvelle langue, même pendant une courte période, peut inciter les stagiaires à s‘intéresser de manière pratique non seulement aux mécanismes de l‘enseignement et de l‘apprentissage avec des étudiants multilingues, mais aussi, de manière plus critique, à reconnaître les relations de pouvoir systémiques entre les langues et les apprenants de langues dans les écoles.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0080.002
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.003

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 head0.049
GPT teacher head0.416
Teacher spread0.367 · 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 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

Citations10
Published2023
Admission routes1
Has abstractyes

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