Beyond methods in language teaching : a qualitative study of teachers' theoretical and practical knowledge of communicativeness in language teaching
Bibliographic record
Abstract
had to face; and it is only now that I can look back on the process, so filled with worry and hope, frustration and patience, devastation and faith.The former in these duos has been my contribution to the work, and the latter has come from a number of people to whom I owe my deepest gratitude.It is now a pleasure to thank a few special ones, though many more have helped me along this road.I first would like to mention those without whom Carleton and Canada would have never become home.Devon Woods, my supervisor -it has been an honor and a privilege to work with you -the person who ejected the sweetest poisons into my thinking, which I believe are beyond all the knowledge and the theory that we have been toiling over for so long.In both senses, I will always associate 'TLC with you
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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 | 0.030 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".