Translanguaging and Trans-Semiotizing for Critical Integration of Content and Language in Plurilingual Educational Settings
Bibliographic record
Abstract
Arising in Europe in the early 1990s, content and language integrated learning (CLIL) has become a popular educational approach. CLIL involves a dual focus on content and language learning with an additional language used as the medium of instruction. Although CLIL has received much attention and spread widely around the world, there is limited discussion that critically examines CLIL in relation to its core construct of integration between content and language learning. In particular, the phrasing of ‘content and language integrated learning’ gestures towards viewing language and content as separate entities. With these fundamental issues in mind, we discuss ways in which translanguaging pedagogies can provide a fruitful direction towards a critical integration of content and language learning in multilingual settings. With a view to contributing to a dynamic integration of content and language learning, we argue that CLIL pedagogies informed by translanguaging allow fluidity in meaning-making practices and critically re-examine the construct of language in CLIL. This approach responds to recent calls for more critical approaches to CLIL in order to challenge ‘English-only’/target-language-only pedagogies, ‘native-(English-)speakerism’, and unequal power relations between content and language teachers in many CLIL programs. Implications of this approach to CLIL classrooms in diverse settings are also discussed.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".