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Record W4387774456 · doi:10.1075/jicb.23015.sut

Academic achievement of minority home language students with special education needs in English language of instruction and French immersion programs

2023· article· en· W4387774456 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Immersion and Content-Based Language Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsCarleton UniversityUniversity of TorontoMcGill UniversityDalhousie UniversityUniversity of Ottawa
Fundersnot available
KeywordsMathematics educationReading (process)French immersionPsychologyHome languagePedagogyLinguistics

Abstract

fetched live from OpenAlex

Abstract This study explored the academic achievement of students who speak a minority language (ML) at home (i.e., a language other than the official languages of Canada, English and French) and who have special education needs (SEN), in two educational programs that differed in language of instruction: English language of instruction (ELoI), and Early French Immersion (EFI). The proportion of students ( n = 131) meeting the provincial standard in reading, writing, and mathematics and the effect of gender, place of birth, socio-economic status, English proficiency level, and program were analyzed. Writing was the strongest domain, followed by reading and mathematics. ML-SEN students were equally likely to meet the provincial standard whether in ELoI or EFI, and there were few significant predictors of achievement. Participating in EFI did not increase students’ risk of academic difficulty. Additional supports may be beneficial to ML-SEN students in ELoI and EFI programs.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.700
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.373
Teacher spread0.344 · 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