TESL Teacher Educators' Professional Self-Development, Identity, and Agency
Bibliographic record
Abstract
Using the concepts of identity and agency, this Perspectives article discusses my recent efforts of self-development when designing an identity-oriented Teaching English as a second language (TESL) teacher education course around teacher candidates’ semester-long autoethnography writing assignment called “critical autoethnographic narrative” (CAN). It specifically unpacks the ways I negotiated and enacted my identities of teacher educator and researcher of teacher education while I was incorporating identity as the main goal in teacher candidates’ learning. In closing, this article offers recommendations for TESL teacher educators who consider designing identity-oriented courses and suggests some future research directions. À l’aide des concepts de l’identité et de l’agentivité (ou capacité d’agir), cet article de Perspectives illustre mes récents efforts d’autoperfectionnement alors que je concevais un cours de formation d’enseignantes et enseignants d’anglais langue seconde axé sur l’identité, et ce, autour de l’imposition d’un projet d’écriture autoethnographique d’un semestre appelé « exposé autoethnographique critique » à des candidates et candidats à l’enseignement. L’article révèle spécifiquement la façon dont je suis parvenu à négocier et faire valoir mes identités de formateur d’enseignants et de chercheur en éducation d’enseignants alors que je faisais de l’identité le principal objectif de l’apprentissage des candidats et candidates à l’ enseignement. En terminant, cet article offre des recommandations à l’intention des formateurs d’enseignantes et enseignants d’anglais langue seconde qui songent à concevoir des cours axés sur l’identité, et ce, en plus de proposer des orientations futures en matière de recherche.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".