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Record W2463982938 · doi:10.1080/13603116.2016.1184330

French second-language teacher candidates’ positions towards Allophone students and implications for inclusion

2016· article· en· W2463982938 on OpenAlexaffabout
Callie Mady, Katy Arnett, Lin Muilenburg

Bibliographic record

VenueInternational Journal of Inclusive Education · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsNipissing University
Fundersnot available
KeywordsInclusion (mineral)Mathematics educationPerceptionPopulationPsychologyPedagogySecond languageFirst languageLinguisticsSociologySocial psychology

Abstract

fetched live from OpenAlex

In Canada, there is a rising population of K-12 students who speak neither French nor English at home, and who are sometimes expected to learn both of the country’s official languages in school. Applying the lenses of critical theory and positioning theory, this study uses questionnaire and interview data to frame considerations to explore how teacher candidates in French second-language (FSL) teacher education programmes across Canada position these Allophone students and their learning needs over the course of their year in teacher education. Findings showed mostly positive attitudes towards the students, and in some cases, apparent small changes in their perceptions of Allophone students in FSL classes but also revealed some uncertainty about the extent to which these positive positions led to recognition of the unique learning needs of this student population. Such findings raise questions about the potential for true inclusion of these students within the learning environment.

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)0.007
metaresearch head score (Gemma)0.014
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 score0.360
Threshold uncertainty score0.716

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0170.009
Scholarly communication0.0080.002
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.015
GPT teacher head0.349
Teacher spread0.334 · 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

Citations14
Published2016
Admission routes2
Has abstractyes

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