A 'Local' Response to Community Problems? A Critique of Community Justice Panels
Bibliographic record
Abstract
Community justice panels have had a long and varied history and are now established at one level or another in most advanced neoliberal states. They involve local members of the community as volunteers in responding to crime and have been lauded for their potential to reduce offending behaviour and provide a more localised, culturally sensitive approach to crime committed by people from those communities. Despite these claims, they have received relatively little attention from scholars working in the areas of community justice and restorative justice. This article seeks to review two different models of community justice panels. The first are those which have been devised in the United States, and subsequently England and Wales, to involve the community in the ‘fight against crime’. The second are those used in Australia and Canada which seek to minimise the use of a formal criminal justice response to offending behaviour by Indigenous peoples and to facilitate a culturally sensitive approach in those cases in which a formal response is unavoidable. Despite their perceived distinct orientation, this article demonstrates that both models have inherent limitations in attempting to ‘localise’ justice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.076 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".