‘Justice’ and Access for Whom? A Critique of Online Men’s Behaviour Change Programs
Bibliographic record
Abstract
Domestic and Family violence (DFV) is a global issue that significantly impacts individuals and communities. Decades of research, policy and service provision have attempted to prevent DFV, yet the prevalence of DFV remains high in Australia and internationally. Recent service development has focused on perpetrators of DFV via targeted perpetrator intervention programs commonly referred to as Domestic and Family Violence Perpetrator Programs (DFVPP). DFVPP create justice pathways for both people who use abusive behaviour, and the people impacted by abusive behaviour, by ensuring users of violence are held accountable for their behaviour and provided opportunities to address their behaviour, and victim survivors are validated in their experiences of abuse. Prior to the COVID-19 pandemic in 2020 Australian DFVPP were almost exclusively delivered in person, creating access barriers to justice pathways for users of violence in rural, regional and remote settings. The pandemic catalysed the development and implementation of online DFVPP. Consequently, there is a timely opportunity to evaluate the feasibility of online interventions for people who use violence. Online interventions have been presented as a panacea for the access barriers recognised for in-person DFVPP. Drawing on original empirical research, this article interrogates this claim. A qualitative research methodology was employed to interview program participants (n=6) and facilitators (n=11) in online perpetrator programs in NSW. Despite many and varied attempts to include victim survivors in the research, those voices were not captured which is a significant limitation of this study. Interview data was supplemented by quantitative program data regarding attendance and completion rates for online perpetrator programs. The combination of data provides insights about the access to justice pathways online programs provide, as well as the digital access barriers that remain, and the potential for online programs to create unjust outcomes for users of violence and victim survivors.
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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 | 0.063 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.047 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.011 | 0.028 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".