Grappling with wicked problems: teacher professionalism and pedagogical mappings for reparative futures
Bibliographic record
Abstract
Wicked problems confront educators to consider whether K-12 education is fulfilling its promise for more equitable futures. This challenge demands teachers understand how past and present are implicated in the formation of injustice and its repair as central to their professional practice. In this context, we ask: How can the professional educator grapple with a history of systemic violence without being paralyzed or overwhelmed? To answer this question, we draw on our experience as teacher educators in a course on teacher professionalism. More specifically, we use pedagogical mappings to trace the difficult work of teacher candidates and teacher educators in self-examination when dealing with wicked problems in education. We suggest that teachers’ ability to identify the thinking and affective patterns that prevent them from engaging with difficult histories is key in committing to reparative futures. We close by offering three provocations to think about how these patterns can help teacher candidates and teacher educators alike forge a professional identity committed to the repair of educational injustices.
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.071 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.008 |
| 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".