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Record W4210879072 · doi:10.47989/kpdc131

Anti transcarceral grief pedagogy for pandemic times

2022· article· en· W4210879072 on OpenAlexaffabout
Jennifer Poole, Erin K. Willer, Samantha Zerafa

Bibliographic record

VenueJournal of Praxis in Higher Education · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsGriefScholarshipDisenfranchised griefPraxisPunishment (psychology)SociologyPandemicPsychologyPedagogySocial psychologyPolitical sciencePsychotherapistLawCoronavirus disease 2019 (COVID-19)Medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.102
GPT teacher head0.494
Teacher spread0.391 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes2
Has abstractyes

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