Charismatic, competent or transformative? Ontario school administrators’ perceptions of “good teachers”
Bibliographic record
Abstract
Emphasis on issues of social justice and attention to socio-cultural perspectives on learning might be at odds with prevailing conceptions of “the good teacher.” In this paper, we probe the perceptions of “good teaching” among Ontario school administrators. We begin with an investigation into dominant discourses of “good teachers” based on the framework posited by Moore (2004). Next, we examine the context that gave rise to Ontario’s New Teacher Induction Program (NTIP), and how this program shapes perceptions of good teachers and good teaching. Data from interviews with forty-one school administrators shed light on their perspectives on good teachers, which is analyzed in light of the dominant discourses and the governing NTIP policy and practice. The discussion highlights the highly personal nature of perceptions of good teaching, and ways in which Ontario school administrators’ perceptions tend to reinforce dominant discourses. The conclusion raises questions about how new teacher induction programs reinforce dominant discourses, and raises possibilities to allow for alternate discourses to coexist.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.021 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".