Translanguaging and trans-semiotising in a CLIL biology class in Hong Kong: whole-body sense-making in the flow of knowledge co-making
Bibliographic record
Abstract
While translanguaging research has been gaining currency worldwide, calls have been made for deepening its theorisation and providing more systematic pedagogical guidance. To contribute to this discussion, this study is informed by a fluid, distributed, dynamic process view of human meaning-making. Through a fine-grained multimodal analysis of classroom activities and interactions, it elucidates the translanguaging/trans-semiotising practices of an experienced science teacher trying out a CLIL (Content and Language Integrated Learning) approach inspired by the Multimodalities-Entextualisation Cycle (MEC) in a Grade 10 biology class in Hong Kong. Post-lesson interviews and survey indicated that such practices generated a positive impact on the students in the continuous flow of knowledge co-making. Implications of the study for furthering the theorisation and practices of translanguaging/trans-semiotising will be 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".