Caring on the 'Inside': Tales of a Critical Educator in a (More) Punitive Space
Bibliographic record
Abstract
As a secondary-level educator with experiences in formal educational including mainstream, alternative, and programs in male correctional facilities, my learners seem to thrive because they feel cared-for. Ironically, I was never quite sure about what it means to be a caring educator. Through this narrative inquiry, I explore my experience teaching within two maximum security jails, in a metropolitan area of Canada. I explore and reflect on my pedagogical strategies and interactions to argue that caring teaching cannot fully interrupt/counteract the violence imposed by carceral systems but are capable of impacting individuals positively. This study, grounded in the narrative of teaching inside, considers how the procedures/expectations of jails work for and against caring pedagogies. I consider how it feels to teach within, how these learners identify me as a caring educator, and whether the teacher can sustain a caring pedagogy in a criminal justice system that enacts enduring harm.
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.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.040 | 0.051 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.015 |
| 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".