Anti transcarceral grief pedagogy for pandemic times
Bibliographic record
Abstract
COVID-19 has saturated many spaces in loss and grief. Higher education has been saturated too, despite ongoing institutional demands that educators mitigate and manage the grief away. Such demands expose the colonial and carceral logics that operate in much of so-called higher education, logics that may often create what we call ‘transcarceral grief’. Inspired by abolitionist activist scholarship, we understand transcarceral grief as an involuntary response to the surveillance, compliance, discipline, and punishment practices (or carceral logics) that have made education a site of restriction and confinement. Such a lens demonstrates how dangerous many of the ‘must-do’s’ of grief and pedagogy can be and changes how we understand our own pandemic pedagogy. Thus, in this piece, we draw on scholarship, activism, theory, and narrated experiences to identify and work against transcarcerality while teaching/learning with grief in our Canadian and American institutions. Rather than mitigating, managing or recovering from grief, we offer a grief-facing praxis that has the potential to disrupt and re-form how we metabolize grief in higher education. Further, we posit that our anti-transcarceral grief pedagogy has the potential to move us closer to the life-affirming space that we crave more than ever both in and out of the classroom.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.020 | 0.043 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".