MétaCan
Menu
Back to cohort
Record W4407179160 · doi:10.1177/02697580251314901

Apology–forgiveness in restorative justice: Victims’ experiences with justice-involved youth

2025· article· en· W4407179160 on OpenAlexaffabout
Laura MacDiarmid

Bibliographic record

VenueInternational Review of Victimology · 2025
Typearticle
Languageen
FieldPsychology
TopicForgiveness and Related Behaviors
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsRestorative justiceForgivenessEconomic JusticeCriminologyVictimologyPoison controlPsychologyHuman factors and ergonomicsSuicide preventionSocial psychologyPolitical scienceSociologyMedical emergencyMedicineLawChild abuse

Abstract

fetched live from OpenAlex

The exchange of apology and forgiveness in restorative justice is regarded as an important marker of symbolic reparation that is imbued with ritual significance. Despite this, how victims assess the sincerity of such remedial work has received limited empirical focus. It also remains unclear what role, if any, apologies play in precipitating victim forgiveness in restorative justice. The current study prioritizes victims’ experiences in Youth Justice Committees , a programme model of restorative justice in Canada, to explore the apology–forgiveness cycle. Analysis of data collected through 14 semi-structured interviews with victims who participated in 16 restorative justice programmes reveals that, for an apology to be deemed sincere, it requires the youth to accept responsibility for the harm, to express affect that conveys their recognition of the harm, and to take steps to make amends. In many instances, a sincere apology prompted forgiveness from the victim; however, the trajectory from apology to forgiveness was not always sequential. Results are discussed considering factors – such as the role of parents/guardians, age, and offence severity – that mediate the likelihood of apology and forgiveness in restorative justice programmes with justice-involved youth.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.025
GPT teacher head0.385
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueInternational Review of VictimologySame topicForgiveness and Related BehaviorsFrench-language works237,207