“It is just so emotionally and mentally consuming to be a community organizer”: The Emotional Labour of Anti-carceral Activism
Bibliographic record
Abstract
Social justice activism can be an emotional enterprise. While many people become involved due to feelings of anger and frustration about a particular unjust socio-political issue, we contend that these feelings exist in tandem with those of love and care for others (or for a specific community of belonging) and that it is this combination of emotions that helps sustain the desire to work toward positive or transformative social change. We mobilize Hochschild’s (1979, 1990, 2012) concept of emotional labour and extend the literature on the emotional labour of racial justice activists by attending to the emotional and affective politics of grassroots, volunteer, peer-based, and unfunded anti-carceral activist groups in the City of Ottawa, Canada. As most research examines emotional labour in the context of paid social and health care work, our examination of grassroots unpaid activism is a unique contribution. We draw on the qualitative accounts of 25 representatives from 13 Ottawa-based activist groups that were gleaned through focus group interviews held over the course of seven evenings, which provide insight into their emotional motivations for anti-carceral activism, their experiences of emotional burnout, and the strategies they employ to manage the emotional impacts of this work.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.048 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".