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
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.084 | 0.027 |
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".