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French Immersion “So Why Would You Do Something Like That to a Child?”

2016· book-chapter· en· W4249650183 on OpenAlexaffabout
Renée Bourgoin

Bibliographic record

VenueIGI Global eBooks · 2016
Typebook-chapter
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPopularityMythologySociocultural evolutionFrench immersionPsychologyPedagogyImmersion (mathematics)SociologyMathematics educationSocial psychologyArtAnthropology

Abstract

fetched live from OpenAlex

French Immersion (FI) continues to grow in popularity and diversity across Canada. However, the suitability of immersion for academically challenged students has often been questioned. This study explored English teachers' beliefs and practices, particularly as they relate to the suitability of French immersion for various learners. It also explored ways by which English teachers frame issues of accessibility to FI for students at risk for academic difficulties. Data revealed that myths about second language education still permeate the system in ways that potentially impact who as access to the FI program. Findings also highlight that the sociocultural and sociopolitical context of this study influences and is being influenced by beliefs about and attitudes toward second language learning. The widespread existence of beliefs and practices grounded in myths or traditional views about second language acquisition points to a need for greater education about issues that potentially limit access to FI.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.003
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0240.005

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 head0.031
GPT teacher head0.244
Teacher spread0.212 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

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