The Accidental Teacher Educator: Learning to be a Language Teacher Educator within Diverse Populations
Bibliographic record
Abstract
Abstract After spending three years as a secondary science teacher in an affluent Toronto neighborhood, I was surprisingly hired as a Literacy Teacher in my old school district just north of the city. I did not have a regular classroom; instead I was expected to work with as many teachers as I could within a cluster of elementary and secondary schools to, broadly speaking, pay explicit attention to the role of language in learning within the content areas. The purpose of this chapter is to analyze and interpret this part of my educational career by engaging in self-study via personal history; a personal history refers to becoming an accidental teacher educator, by virtue of a unique role as an in-service teacher educator with a language and literacy portfolio. Journals kept over two years reveal that, in many ways, I was a teacher educator before I knew what the term meant and that developing a pedagogy of teacher education with a focus on literacy made me increasingly frustrated with the over-simplified ways in which my school district framed issues of diversity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".