A Study of Volunteers in Community- Based Restorative Justice Programs
Bibliographic record
Abstract
Les programmes de justice réparatrice (JR) axés sur la collectivité dépendent grandement des bénévoles pour accomplir différentes tâches, y compris animer les conférences de cas. Nous avons mené un sondage auprès de76bénévoles de 12 programmes de JR en Colombie-Britannique, au Canada, afin de: (1) définir les caractéristiques des bénévoles; (2) documenter leur participation à la JR; (3) mesurer les motivations qui les poussent à travailler bénévolement; (4) explorer les compétences qui, à leur avis, sont utiles; (5) documenter la formation qu’ils ont reue; et (6) déterminer les facteurs influant sur leur satisfaction quant aux rôles qu’ils jouent. L’étude a été guidée par un modèle conceptuel du processus de bénévolat en JR. Nous avons découvert que les bénévoles en JR sont surtout des femmes blanches d’un certain âge. Les bénévoles sont principalement recrutés par le bouche à oreille et sont motivés par leur poursuite des idéaux associés àla JR. Même s’ils possèdent de nombreuses compétences et qualités, les bénévoles ont reçu une formation leur permettant d’offrir un éventail de services. Enfin, les bénévoles sont en général satisfaits de leurs rôles dans les programmes de JR. Ces résultats ont des conséquences sur le recrutement des bénévoles, leur formation et le maintien de l’effectif. Mots clés: justice réparatrice, bénévolat, animateur en médiation Community-based restorative justice (RJ) programs rely heavily on volunteers to perform a range of duties, including facilitating case conferences. We surveyed 76 volunteers from 12 RJ programs throughout British Columbia, Canada, in order to (1) identify the characteristics of volunteers, (2) document their involvement in RJ, (3) measure their motivations to volunteer, (4) explore the skills they perceive to be useful, (5) document the training they receive, and (6) determine the factors that influence satisfaction with their roles. This study was guided by a conceptual model of the RJ volunteer process. We found that RJ volunteers comprise primarily older Caucasian women. Volunteers were mostly recruited by word of mouth and were motivated by their commitment to RJ ideals. Although they brought a wealth of skills and qualifications, volunteers were trained in order to provide a range of services to programs. Finally, volunteers were generally satisfied
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".