Bibliographic record
Abstract
Introduction Conceptualizing and implementing teacher education programming for teachers who work with students who do not use the school's language of instruction as their primary language is a complex task. At the heart of such programming, we usually find a set of courses that examine such topics as the teaching of listening, speaking, reading, and writing skills; content-based language teaching; curriculum planning; classroom management; and evaluation strategies. Increasingly, these methodology courses also include observation of and reflection on language classrooms, peer teaching with feedback, and cooperative learning activities. However, what isn't often discussed is the impact that the arrival of second or other language students has on a school's linguistic, cultural, and learning environment outside the language classroom or the linguistic and racial tensions that sometimes arise as these students attempt to integrate into the school community. Responding to changes in the school learning environment and linguistic and racial tensions between students is not easy, and school staff members often turn to their language teachers to help them think about effective ways of moving forward. This chapter is about preparing language teachers to respond effectively to the complexities of working across linguistic, cultural, and racial differences in multilingual schools so that they can show leadership around such issues as language choice, linguistic discrimination, and racism. In thinking about how to prepare my own teacher education students for this kind of leadership, I have begun to experiment with ethnographic playwriting and performed ethnography.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".