“Help me see”: Recognizing the importance of a core curriculum under observation in an english teacher education program for global society
Bibliographic record
Abstract
Classroom observation represents itself as an important element to teachers’ education. Within diff erent contexts, observation has been used to understand and refl ect on a classroom and on the role of the teacher and it has been adopted as a partial requirement on supervised practicum, continued teachers’ education programs, selfevaluation and others. In order to contribute to the understanding of this practice and to the education of future English language teachers for the global society, this article outlines the practice of observation that has been conducted in a group of students at York University, Glendon Campus, in Toronto. This group had taken D-TEIL program which aims at teaching English in an international context and during their undergraduation, they have been through steps that they would need to develop strategies to observe classrooms teachers and even themselves as student-teachers in Cuba. As in Kumaravadivelu (2012), it has been suggested that his macro-strategies function as an initial point of reference to give a special att ention to observation, and consequently to embed curriculum elements into the English language teachers’ education.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".