Between Grief and Relief: The Emotional Impact of Incarceration on Loved Ones
Bibliographic record
Abstract
Abstract Scholars have detailed the impacts of imprisonment on loved ones through concepts such as collateral consequences, secondary prisonization, and symbiotic harms. However, these frameworks often overlook the complex range of emotions loved ones experience when their friend or family member is incarcerated. Drawing on 181 longitudinal interviews with 29 loved ones of incarcerated men across Canada, we use the sociology of emotions to explore the plurality of loved ones’ emotional relations toward imprisonment. We highlight three complex and overlapping emotional orientations that loved ones experience following the incarceration of a friend or family member: (1) ambiguous loss, (2) anticipated burdens, and (3) respite. We document how loved ones’ experiences of incarceration are characterized by emotional ambivalence, as they try to reconcile feelings of loss and longing for their loved ones’ return, the “frustrating” and “illogical” challenges they expect to be burdened with leading up to and upon release, and the relief that can accompany a much-needed break from the ongoing worry about their loved ones’ precarious lives in the community. Our discussion connects these ambivalent orientations to broader sociological themes of gender, criminal justice, the welfare state, and emotions work. We conclude by outlining areas 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| 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".