MétaCan
Menu
Back to cohort
Record W4405495211 · doi:10.1075/jicb.24004.azp

Revising expectations

2024· article· en· W4405495211 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.

Bibliographic record

VenueJournal of Immersion and Content-Based Language Education · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of Alberta
FundersUniversidad Pública de NavarraUniversidad de Navarra
KeywordsContent and language integrated learningFluencyPsychologySocioeconomic statusPronunciationVocabularyPost hocPedagogyMathematics educationMedicineLinguisticsSociologyDemographyForeign languageDentistryPhilosophyPopulation

Abstract

fetched live from OpenAlex

Abstract Research evidence predominantly based on studies with older learners suggests that Content and Language Integrated Learning (CLIL) instruction yields significant language gains when exposure exceeds 300 hours ( Muñoz, 2015 ). However, the impact of high-intensity CLIL on young learners’ oral proficiency remains underexplored. This study examined fluency, pronunciation, and productive vocabulary measures in young L1-Spanish learners (mean age = 10.46) across four groups: non-CLIL ( n = 23), low-CLIL ( n = 21), high-CLIL ( n = 32), and a younger high-CLIL group ( n = 32; mean age = 9.84) with 0, 707, 2473, and 2164 CLIL hours, respectively. Socioeconomic status and extramural exposure were controlled. Intraclass correlations, Kruskal-Wallis, post-hoc, and Friedman tests were conducted. Significant advantages were limited to both high-CLIL groups over the non-CLIL group at the vocabulary level, providing policymakers with empirical evidence about the markedly different outcomes of high, and low-CLIL programmes in relation to oral gains with young learners.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.276
Teacher spread0.248 · 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