The Impact of Holistic Justice on the Long‐Term Experiences and Wellbeing of Mass Human Rights Violation Survivors: Ethnographic and Interview Evidence From Kosova, Northern Ireland and Albania
Bibliographic record
Abstract
ABSTRACT Research highlights the long‐term collective effects of mass human rights violations (MHRVs) on survivors’ wellbeing. This multi‐method, multi‐context paper combines the social identity approach (SIA), transitional and social justice theories and human rights‐conceptualised wellbeing to propose a human rights understanding of trauma responses and experiences in the context of MHRVs. In Study 1, ethnographic research in four locations in Kosova, 5 years post war indicates that lack of perceived conflict‐related and social justice is experienced as a key contributor to survivors’ individual and collective wellbeing. In Study 2, 61 semi‐structured interviews with MHRVs survivors from post‐war Kosova, post‐conflict Northern Ireland and post‐dictatorship Albania two to three decades post conflict also show that such justice experiences inform wellbeing. These studies illustrate the importance of expanding the SIA to health and trauma theories by taking account of a human rights‐conceptualised wellbeing as well as adopting a holistic analysis of justice perception.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".