Promises of change: Envisioning new lives in partner abuse intervention programs
Bibliographic record
Abstract
Abstract How do interventions with violent offenders instill a desire for change? This article uses the case of partner abuse intervention to examine the discourses and subjectivities that emerge in intervention programs, as well as their potential impact on desistance from violence. Drawing on ethnographic observations and interviews conducted at two partner abuse intervention programs (N = 103), I find that through interventions, some participants build narratives of transformation that allow them to make sense of their violence, believe in their capacity for change, and start envisioning nonviolent lives. Bringing into dialogue literature on the carceral state, narrative criminology, desistance, and domestic violence, I examine the role that these narratives can play in the prevention of domestic violence. My findings reveal a fundamental paradox of partner abuse intervention: On the one hand, narratives of transformation can put program participants on the track toward desistance. On the other hand, these same narratives—which focus on avoiding dependency and achieving self‐sufficiency—foster interventions that are fundamentally unequal and reinforce the gendered beliefs that support domestic violence. I discuss implications for violence prevention and understandings of the operations of the carceral state.
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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 | 0.026 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.035 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".