"It's not just for me, it's also for my community": Toward the Decommodification of Self-Care Through Racialized Activists' Care Practices
Bibliographic record
Abstract
To promote change, activists must confront the suffering of their communities, but this process can weigh heavily on them and contribute to burnout. The burden is even heavier for racial minority activists who combat additional stressors associated with discrimination. While many activist organizations promote community care, their conceptualization of community wellness competes with the individual wellness promised by luxury self-care products, inaccessible to those facing financial barriers. The present study challenges this buy-in model of self-care. Using participatory qualitative methods and grounded theory, I have examined how racialized activists in Saskatoon develop their own care practices. Driven by a desire to live their values, participants developed their care practices in five stages: Developing Activist Values, Belonging, Participating in Community Care, Protective Self-Care, and Restorative Self-Care. By the end of the process, participants were sustaining long-term wellness strategies by exercising their values with the support of their communities. The results present a conceptualization of personal wellness that resists commodification by being grounded in community, personal values, and political defiance. The work discusses the implications for conceptualizations of well-being, and directions for future research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".