Mass incarceration and symbiotic harms: How families and loved ones experience the criminal justice system
Bibliographic record
Abstract
ABSTRACT Families constitute vital sources of support to the approximately two million people currently incarcerated in the United States by contributing financial assistance, providing emotional support, and promoting desistance. Existing research on families focuses on the experiences of partners or children rather than parents or adult siblings, and particularly on how family support can reduce recidivism. This paper incorporates the theoretical approach of symbiotic harms, which emphasizes the flow of harms into and out of the criminal justice system. Drawing on observations of three national, virtual support groups and 21 in-depth interviews with family members, this study investigates shared experiences across the criminal justice system and agentic responses to harm, from small acts of resistance to activism. The data reveal symbiotic harms in four key areas: financial and medical burdens, emotional adjustments, status disclosure, and negative experiences within prison walls. This research addresses a question largely unanswered by the existing symbiotic harms literature on how agency is defined and exercised. The data illustrate an important case study of the meanings that support group participants attach to their practices of agency: self-care as empowerment, reflexive engagement with processes of stigmatization, strategies for counteracting harms, and reframing setbacks as blessings.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 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".