Resilience in the Face of Victimization: A Bourdieusian Analysis of Dealing With Harms and Hurts Among Street-Involved Individuals Who Smoke Crack Cocaine
Bibliographic record
Abstract
Victimization is a significant issue for street-involved individuals who smoke crack cocaine. Presently, there is a shortage of resilience research exploring practices that may insulate crack-smoking individuals from victimization or mitigate the effects of such experiences. Through a Bourdieusian lens, this qualitative study examines responses to victimization and the types of coping strategies utilized among ( n = 30) street-involved individuals who regularly smoke crack in Southern Ontario, Canada. A resilience framework is used for analysis—a novel approach in addictions research, since drug-using individuals are generally left out of this realm of investigation. Findings show that participants mobilize their resources and capacities to “rebound” from victimization in the “street field” (Shammas & Sandberg, 2016), through various practices. These individuals rely on their own embodied competencies as well as the “street social capital” (Ilan, 2013) available to them through their networks, in order to deal with victimization. Resilience is a complex process that needs to be continually (re)constituted for participants, owing to their lack of capital and the structural limitations of the street field. In conclusion, participants display resilience through their sense of determination and ongoing agency in navigating constraints and seizing opportunities to avoid, manage, and confront chronic victimization in the street field.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".