MétaCan
Menu
Back to cohort
Record W1536242968 · doi:10.26522/tl.v4i3.276

A Critical Commentary

2008· article· en· W1536242968 on OpenAlexvenueaboutno aff
Josée Makropoulos

Bibliographic record

VenueTeaching and Learning · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicFrench Language Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsFrench immersionAP French LanguageMathematics educationPsychologyLinguisticsPedagogySociologyLanguage educationPhilosophy

Abstract

fetched live from OpenAlex

Early French Immersion (EFI) programs were introduced in Canada in 1965 for students from Anglophone families where neither one of their parents spoke or understood much French. Unlike other bilingual programs, the immersion approach introduces French for the instruction of academic subjects and promotes the use of French as the language medium for classroom interactions. The EFI option is usually offered from the onset of elementary school, in Kindergarten or Grade One, and provides equal instruction time in English and French after primarily exposing students to the target language of instruction. Middle French Immersion (MFI) and Late French Immersion (LFI) are geared for older children who did not begin EFI programs, and respectively begin in Middle School (Grades 4 or 5) and at the Intermediate Level (Grades 7 and 8). By the 1980s, optional French immersion programs were available across Canada to families wishing to provide their children access to bilingual instruction passing through English first and French as the second official language.

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)0.019
metaresearch head score (Gemma)0.144
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.062
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.144
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.002
Science and technology studies0.0070.009
Scholarly communication0.0070.008
Open science0.0070.005
Research integrity0.0620.058
Insufficient payload (model declined to judge)0.0260.009

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 head0.036
GPT teacher head0.368
Teacher spread0.332 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations16
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueTeaching and LearningSame topicFrench Language Learning MethodsFrench-language works237,207