Teacher education as a scene of (dis)repair: a critique of therapeutic practices
Bibliographic record
Abstract
Teacher attrition has become a pervasive international issue with research documenting teachers leaving the profession as an effect of several factors including poor working conditions and flawed policy contexts. Such research has been helpful in drawing attention to how the harsh realities of classrooms and schools can disillusion teachers, creating stress, burnout, and potentially a desire to leave the profession. It has also led to an emphasis on well-being and mental health in teacher education programmes and the adoption of practices such as self-care planning, animal therapy, and resilience workshops. Such initiatives adopt the ideas, practices, and language of psychological therapy and as such can be characterised as “therapeutic” practices. Critiquing therapeutic practices in teacher education, the author argues that an excessive focus on the teacher’s inner reality distracts from the ethico-political dimension of educational work – care for the world (of education) that brings us together as educators – with consequences for the profession and for democracy. Drawing on Honig’s (2017) concept of “public things,” the author offers conceptual resources with which teacher educators might consider how self-care is entangled with care for the world, inviting a (re)imagining of teacher education as a site of repair and renewal of that world.
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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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.027 | 0.177 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.017 | 0.026 |
| Insufficient payload (model declined to judge) | 0.003 | 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".