Thematic patterns, Cognitive Discourse Functions, and genres
Bibliographic record
Abstract
Abstract As CLIL is developing into an established discipline, it is timely to deepen the theorizing of integration of content and language, particularly in CLIL assessment. To illustrate the challenges, a representative example of a high-stakes CLIL biology assessment task in Hong Kong will first be presented. An Integrative Model for CLIL will then be proposed and applied to illuminate the demands of the assessment task and diagnose a sample student performance. The Integrative Model is developed by integrating genre and register theory ( Martin & Rose, 2008 ), Cognitive Discourse Functions ( Dalton-Puffer, 2013 ), thematic patterns theory ( Lemke, 1990 ), Concept-and-Language-Mapping (CLM) Approach ( He & Lin, 2019 ) and translanguaging/trans-semiotizing theories ( Garcia & Li, 2014 ; Lin, 2019 ). To further illustrate the utility of the Model, a range of possible assessment-for-learning ( Black et al., 2003 ) CLIL task examples designed by the authors will be presented. The article will conclude with implications for CLIL pedagogy and assessment.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".