Bibliographic record
Abstract
It was with high expectations that we read Lasagabaster and Sierra’s (2010) contribution to this Journal, in which they set out to differentiate between CLIL and immersion. While we agree with the need to resolve the confusion surrounding these two approaches, we were disappointed with the manner in which an intended ‘clear-cut’ distinction was attempted. Working from the Spanish context, yet claiming universal applicability, Lasagabaster and Sierra (hereafter L&S) found more differences than similarities between CLIL and immersion. It not only pains us to see that a qualitative distinction is reduced to the mere quantification of differences, but after critically examining L&S’s argumentation, we have found it to be neither clear nor universally tenable. Without substantiation, Lasagabaster and Sierra (ibid.: 370) list five principles they claim CLIL and immersion share: ... We were much surprised at Similarity 2. It no longer fits the changing demographics in Spain, Canada, or elsewhere (Lyster and Ballinger 2011: 281): Basque-medium schools in the Basque Autonomous Community have both Spanish and Basque NS students; Catalan immersion programmes in Catalonia can have as many as 30 per cent native Catalan-speaking students; even in Quebec, classrooms are increasingly made up of French NS, English NS, and French-English bilingual students; this equally goes for Welsh- and Irish-medium education in Wales and Ireland, respectively. Also, to tip the numerical balance, we can think of a few more similarities: overt support for the students’ L1, the aim for additive bilingualism, integration of language and content, etc.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.083 |
| Scholarly communication | 0.029 | 0.061 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.011 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
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".