Just Caring? Supervisors Talk About Working With Incompetent Teachers
Bibliographic record
Abstract
The administrative strategy of inducing the exits of teachers whose performance has been judged incompetent has received little scholarly attention. Because of the complex moral issues involved, we view situations in which induced exits are the means to removing incompetent teachers from classrooms as a crucible for ethical questions about the enactment of caring and just administrative leadership in education. We take as the framework for our analysis the contention of some scholars that there is a necessary complementarity between - or integration of - caring and justice in both theory and practice. Drawing on data, particularly interviews, from a recent study that explored the supervisory processes leading to forced resignations, our purpose is to show how some supervisors' accounts suggested a blend of caring and justice while others did not. We hope this analysis invites reflection on what constitutes ethical supervisory practice in a difficult and morally perplexing area of administrative leadership.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".