Face to Face: A Reflexive Thematic Analysis of Victim-Offender Mediation
Notice bibliographique
Résumé
Restorative justice (RJ) practices have become increasingly accepted as viable alternatives to the retributive justice system in Canada and the world as a whole. RJ has been appropriated from Indigenous knowledge, which is important to recognize. One example of RJ in our present colonial system is victim offender mediation (VOM), which involves the offender(s) meeting the victim(s) in the presence of a trained mediator. I utilized reflexive thematic analysis as described by Braun & Clarke (2022) to study nine cases of VOM in Minneapolis, USA, and Winnipeg, Canada. My analysis aimed to discover what participants say about their experiences with VOM. By analyzing interviews with victims and offenders both prior to and several months following mediation, as well as observing the mediations, I constructed a model of the mediation process. This model consisted of what happens prior to mediation (i.e., what motivates victims and offenders to participate), processes that occur during the mediation, and two types of satisfaction that may be present following mediation. Motivations to participate were found to be of two different orientations: self-focused and relationship-focused. Within the walls of the mediation room, expected as well as unexpected themes were discovered. Participants of course attempted to satisfy their pre-mediation motivations, an apology often occurred, and a healing connection was forged between the parties. However, sometimes a previously self-focused or unengaged offender was able to be pulled into a more relationship-focused orientation by having a compelling experience with the victim. This shift involved the challenging of one’s expectations about the mediation or the other involved party, whereby a corrective emotional experience occurred. Finally, two outcome themes were delineated: simple satisfaction, and healing through relationship. My findings were compared to previous research, and areas for potential future study as well as implications for practice were discussed.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».