CLIL and immersion: how clear-cut are they?
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it