Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.049 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".