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
This volume presents the second edition of the Communicative Orientation of Language Teaching (COLT) Observation Scheme. Since the book’s original publication, COLT has become well established as a research instrument in L2 teaching and learning. This new edition brings COLT into the 21st century by introducing digital versions of the scheme and describing how advances in technology have made the collection, coding, analysis, and synthesis of classroom data faster and more efficient. Enhancements include the availability of web-based platforms for the coding, sharing and storage of data, the application of artificial intelligence in the coding of classroom observation data, numeric coding systems, and ongoing work in the use of automatic speech recognition for faster transcription. The volume has a similar organizational structure to the original COLT book with the addition of a new chapter on Digital COLT (Part A), a new section on Numeric COLT (Part B), and an expanded final chapter that includes updated summaries reporting on the use of COLT for a wide range of research purposes in diverse L2 contexts. As with the first edition, the material is presented in a user-friendly manner with examples, illustrations and hands-on activities throughout. It is intended for both novice and experienced researchers investigating teaching and learning in L2 classrooms and in teacher education/reflective practice research. The companion web site with interviews and a video tour can be found at: https://benjamins.com/sites/lllt.60
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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