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Record W2255166237 · doi:10.1075/jicb.3.2.04ser

The University of Ottawa Immersion Program

2015· article· en· W2255166237 on OpenAlexaffabout
Jérémie Séror, Alysse Weinberg

Bibliographic record

VenueJournal of Immersion and Content-Based Language Education · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFrench immersionFrenchAP French LanguagePedagogyPsychologyDisciplineImmersion (mathematics)SociologyMathematics educationForeign languageLinguisticsSocial science

Abstract

fetched live from OpenAlex

This article reports on case studies of university students participating in the University of Ottawa French Immersion Study (FIS) program, the largest tertiary immersion option in Canada. This program allows Anglophone students to complete an undergraduate degree while taking academic courses in their second official language (French). Semi-structured interviews with case study participants were used to analyze immersion students’ accounts of their experiences within this program. Findings focus on the interactions offered to FIS students and their role in shaping students’ identities and orientation to French and Francophones. Through the FIS, students are able not only to acquire linguistic and disciplinary knowledge, but also engage, often for the first time, in in-depth and daily interactions with the French community. As a result, their discourse reflects an identification and coming together with the Francophone community thereby seeming to bridge a gap between English speakers and French speakers typically found in elementary and secondary immersion 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.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.429

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0110.003
Scholarly communication0.0030.001
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0250.002

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 head0.032
GPT teacher head0.253
Teacher spread0.221 · 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 designObservational
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

Citations5
Published2015
Admission routes2
Has abstractyes

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Same venueJournal of Immersion and Content-Based Language EducationSame topicSecond Language Learning and TeachingFrench-language works237,207