The Future Is Now: Preparing a New Generation of CBI Teachers.
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
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.
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.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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