Content-based language teaching: Convergent concerns across divergent contexts
Bibliographic record
Abstract
This article serves as the introduction to this special issue of Language Teaching Research on content-based language teaching (CBLT). The article first provides an illustrative overview of the myriad contexts in which CBLT has been implemented and then homes in on the five studies comprising the special issue, each conducted in a distinct instructional setting: two-way Spanish—English immersion in the USA, English-medium ‘nature and society’ lessons taught at a middle school in China, English-medium math and science classes in Malaysian high schools, English-medium history classes in high schools in Spain, and ‘sheltered instruction’ classes for English language learners in US schools. In spite of such divergent contexts, the five studies converge to underscore the pivotal role played by teachers in CBLT and the concomitant need for professional development to support them in meeting some of the challenges specific to CBLT.
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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 | 0.090 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.002 | 0.004 |
| 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 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".