Mind the Gap: How a Project in Alberta Attempted to Narrow the Gap Between Classroom Teachers and Language Education Research
Bibliographic record
Abstract
This article presents the development, rollout, and subsequent uptake of the Al- berta Teachers of English as a Second Language (ATESL) document Best Prac- tices for Adult English as a Second Language (ESL)/Language Instruction for Newcomers to Canada (LINC) Programming in the light of literature on teacher engagement with second-language acquisition and teaching research. The first part of the Best Practices project was designed to develop a set of best practice statements of principle developed in collaboration with language education ex- perts in Alberta and through the professional literature, as well as with ESL teach- ers across the province. In the second part of the project, we facilitated the uptake of the principles by teachers and administrators throughout Alberta.Cet article présente le développement, la mise en œuvre et l’adoption subséquente du document des enseignants albertains de l’ALS portant sur les meilleures pra- tiques pour l’ALS aux adultes et le programme cours de langue pour les immi- grants au Canada (CLIC) et ce, à la lumière de l’implication des enseignants dans la recherche portant sur l’acquisition et l’enseignement de langues secon- des. La première partie du projet des meilleures pratiques visait le développement d’une série de déclarations de principes élaborées en collaboration avec des ex- perts en enseignement de la langue, appuyées par la littérature spécialisée et des enseignants en ALS de partout en Alberta. La deuxième partie du projet visait à faciliter l’adoption des principes par les enseignants et les administrateurs à travers l’Alberta.
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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 | 0.085 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.056 | 0.040 |
| Scholarly communication | 0.026 | 0.011 |
| Open science | 0.009 | 0.031 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 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".