A Qualitative Examination of Character Strengths and Virtues in Intergroup Reconciliation
Bibliographic record
Abstract
This paper argues that the VIA Classification of Strengths and Virtues (Peterson & Seligman, 2004) provides a useful foundation for Character Education (CE) generally and an effective common language for the understanding of Reconciliation specifically. It presents recent literature examining the prototypical elements of CE (McGrath, 2018), and the application of the VIA Classification to the study of peace and inter-group conflict (Niemiec, 2022). It then goes on to report seven interviews with experienced practitioners working in three reconciliation centres (Rose Castle Foundation; the National Centre for Truth and Reconciliation; and Reconcilers Together), located in the United Kingdom, Canada, Egypt, and Belgium. Qualitative analysis of the data yielded 14 themes, and 32 sub-themes or codes, related to the use of Character Strengths in the work of reconciliation. While the participants represented different approaches to the reconciliation process, their ability to easily identify constructs from the VIA Classification in the practice of reconciliation supports the usefulness of the VIA nomenclature in providing a common language for both the understanding of and educating for reconciliation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".