Thematic patterns, Cognitive Discourse Functions, and genres
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it