Bibliographic record
Abstract
Transitions in ESL contexts generally refer to those linking words placed between sentences and between paragraphs. Transitions in writing (and in speaking) are helpful; they facilitate coherence and cohesion when used correctly. Understand- ing them when reading allows us to join the writer in seeing why and how idea B follows idea A. In this commentary I argue that transitions serve the same func- tion in the unfolding of ESL instructional experiences. Instructors have pre- planned the choreography, but is it transparent to the students? Should it be? What if we conceive of the ESL lesson as text—an essay in particular?En anglais langue seconde, quand on parle de transitions, on fait généralement référence aux mots de liaison entre une phrase et une autre, et un paragraphe et un autre. Les transitions sont utiles à l’écrit (et à l’oral) ; quand elles sont bien employées, elles augmentent la cohérence et la cohésion du message. Quand le lecteur comprend les mots de transition, il voit la suite dans les idées de l’auteur. Dans cet article, j’affirme que les transitions servent la même fonction dans le déroulement des cours d’ALS. Les enseignants prévoient la chorégraphie, mais est-elle transparente pour les élèves ? Devrait-elle l’être ? Et si on concevait le cours d’ALS comme un texte – une dissertation, en fait?
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".