Constructing a european educational product : a critical genealogy of CLIL research
Bibliographic record
Abstract
An extensive body of research has credited Content & Language Integrated Learning (or CLIL) with numerous (extra)linguistic benefits since its inception under the auspices of the European Union in the 1990s. For a few years though, there have been a growing number of scientific controversies on CLIL. Criticisms fundamentally revolve around the elitism of CLIL, which has been obscured in many studies (Bruton 2013). The purportedly difference between CLIL and immersion has also been sharply debated (Cenoz et al 2013). Last but not least, CLIL would have been mainly developed by EFL specialists (Cenoz et al 2013). In this contribution, I aim to analyse research’s enthusiasm for CLIL in the light of CLIL ideological context of birth (Heller & McElhinny 2017). To do so, I analyse a key document on CLIL, i.e. the Marsh (2002) Report. This EU-funded report was authored by the founding father of CLIL, David Marsh. Since 2002, the report has been abundantly quoted and validated as a scientific source in CLIL research. Notably, I unveil how the report covertly constructs selective learner characteristics as prerequisites for future CLIL learners, despite its claim that CLIL is more egalitarian than immersion. In my conclusions, I discuss my findings in the light of language ideologies and suggest new angles of research for CLIL. References Bruton, A. (2013). CLIL: Some of the Reasons Why… and Why Not, System, 41(3). 587-597. Cenoz, J., Genesee, F. & Gorter, D. (2013). Critical Analysis of CLIL: Taking Stock and Looking Forward, Applied Linguistics, amt011. 1-21. Heller, M. & McElhinny, B. (2017). Language, Capitalism, Colonialism: Toward a Critical History. Toronto: University of Toronto Press. Marsh, D. (2002). CLIL/EMILE-The European Dimension: Actions, Trends and Foresight Potential. Public Services Contract DG EAC 36 01 Lot 3. Brussels: European Commission.
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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 | 0.028 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.009 | 0.054 |
| Scholarly communication | 0.025 | 0.031 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".