Grappling with wicked problems: teacher professionalism and pedagogical mappings for reparative futures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it