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 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.
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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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