Bibliographic record
Abstract
Abstract CLIL focuses on the integration of content learning and additional language learning. However, it is increasingly recognized that the re/presentation and communication of discipline-specific content involve not only language, but also other semiotic modes (such as visuals and gestures). This is accelerated by the advancement of digital technologies and multiplicity of communication channels in recent years. This article points out the urgent need to revisit and reconceptualize the roles of “language” in CLIL. It argues that, to prepare students for the multimodal communication landscape in today’s societies and to truly value their linguistic and semiotic diversity in learning, the “language” dimension in CLIL needs to be reconceptualized as a multimodal dimension, and CLIL classroom practices need to adopt an updated pedagogy of multiliteracies ( New London Group, 1996 ) rather than focusing on “mere language” practice. The article reviews the recent development of theories and studies of multimodality and trans-semiotics and discusses their implications for what to teach and how to teach in today’s CLIL classrooms. It proposes the notions of translanguaging and trans-semiotizing to emphasize a dynamic and dialogic process of meaning (co)making process drawing on multiple linguistic and semiotic resources to enable students to both gain access to and critically engage in meaning/knowledge co-making/co-design. Ultimately, it aims at reconceiving CLIL to contribute to a more equitable school and classroom culture.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".