Emotions and anti-carceral advocacy in Canada: ‘All of the anger this creates in our bodies is also a tool to kill us’
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
Over the past three decades, Canada has expanded its capacity to confine citizens in ways that disproportionately affect Black, Indigenous and People of Colour (BIPOC) communities and people grappling with mental health and substance use issues, as well as poverty and homelessness. Carceral expansion, however, is not restricted to increasing institutional capacity; it also entails mechanisms to govern vulnerable people through the broader community-based carceral system. Based on a series of focus group interviews with representatives from over a dozen different community-based advocacy groups in Ottawa, Canada, this article examines the emotional labour these radical activists employ in their anti-carceral advocacy work. We explore how emotions and affects structure the strategies mobilised by these groups, and how they enable these advocates to resist carceral expansion. We also examine how critics of the anti-carceral position held by our participants tend to frame their interventions in ways that seek to delegitimise these activists as overly emotional or irrational in their denunciation of carceral violence, even as advocates remarked how their radical activist positions on penal abolition have been co-opted by proponents of police reform. This is revealing of the ways in which the emotional states of actors with fewer resources and authority can be mobilised by those in positions of relative power, transforming the emotional landscape of contestation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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