Is ‘good’ really good? Exploring internationally educated teacher candidates' verbal descriptions of their in-school experiences
Bibliographic record
Abstract
In this paper we offer an incident that exemplifies one of multiple strategies internationally educated teacher candidates (IETC) use to survive practicum experiences. More specifically, we present an incident that demonstrates teacher candidates' strategic way of using words, such as ‘good’ and ‘fine’, to disguise true feelings about experiences of their teaching placements in schools. We also offer related strategies used by these IETC to negotiate and nurture classroom relations with peers and instructors at the Faculty of Education. Here we argue that within teacher education programmes, especially in the practicum component and other situations that are shaped by it, language is an active force that is used, on the one hand, by associate teachers to control and prevent teacher candidates from changing established norms and values; on the other hand, however, language is used by teacher candidates to defend themselves against being controlled. We present conclusions about this incident drawing from our three years of working with teacher candidates from cultures and languages that are different from and often marginalised by those of the Canadian mainstream. In our discussion, we employ studies in communication and language use to illustrate the complex meaning entailed by this incident.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".