Modelling plurilingual instruction through a crosslinguistic-communicative task sequence
Bibliographic record
Abstract
Abstract This study aims to bridge the gap between ‘communicative’ and ‘plurilingual’ approaches, by providing a means for teachers to integrate learners’ plurilingual repertoires when teaching an additional language (Lx). We developed a model of crosslinguistic instruction embedded within the task-based language teaching approach. It consists of a 4 stages task sequence: input-based task, crosslinguistic consciousness-raising task, output-based task, and recap of the sequence. An iterative process of field testing and analysis ( Harvey & Loiselle, 2009 ) allowed us to refine the model: researchers in-depth analysis (functional field test), implementation of the model in Lx classrooms (empirical field test) and experts assessment (second functional field test). Participant perceptions and evaluations provide an overview of their appreciation of different aspects, which lead to the current version of the model.
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How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".