Bibliographic record
Abstract
Content-based instruction (CBI) is not a new term for foreign language teachers. By some accounts, CBI has been employed since the ancient Akkadians adopted Sumerian as the medium of instruction to educate their young in science and religion (Mehisto, Frigols, and Marsh 2008, 9). In the modern era, content-based approaches to language instruction have been employed in various forms since at least the 1960s, when Canadian language educators began teaching academic content in French to English mother-tongue children (Stoller 2008). Yet a large proportion of today’s teachers of English as a Foreign Language (EFL) have never had the opportunity to try out CBI in their own classrooms—and many of these teachers may lack key professional knowledge and skills that are critical to successful CBI teaching. At the same time, CBI approaches are playing an increasingly prominent role in institutional, national, and regional foreign language curricula, as for example in various Content and Language Integrating Learning (CLIL) projects that are being implemented in Europe (Fernandez Fontecha 2009; Lorenzo, Casal, and Moore 2009; Naves 2009; Seikkula-Leino 2007; Serra 2007). The purpose of this article is to consider ways that language-teacher education programs can better prepare future CBI teachers. After providing a brief rationale for why CBI approaches are particularly relevant in the 21st century, I will consider the competencies and skills that the language teachers of tomorrow will need to effectively integrate content and language instruction in their courses.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".