Bibliographic record
Abstract
Abstract Content‐based instruction (CBI), the purposeful integration of language and content in second/foreign language teaching, is the focus of the current entry. CBI draws inspiration from immersion programs in Canada and the work of Bernard Mohan, which laid the foundation for CBI models that can be found today in second/foreign teaching settings worldwide. The entry describes various approaches of CBI, ranging from language‐driven courses, such as theme‐based instruction, to content‐driven models, such as English as medium of instruction (EMI) and content language‐integrated learning (CLIL). In addition, the entry presents examples of actual CBI programs designed to meet the specific needs of students of different ages, needs, learning outcomes, and settings and, in particular, describes features of academic language that can be taught in CBI programs. Decades of CBI research have revealed that current challenges are the day‐by‐day implementation of CBI courses and programs and the on‐going need for professional development of both language and content teachers. As we look to the future, these two issues, and others, continue to motivate work in CBI.
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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".