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Record W4403538177 · doi:10.1002/ejsp.3124

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

2024· article· en· W4403538177 on OpenAlexfundno aff
Blerina Këllezi, Juliet R. H. Wakefield, Mhairi Bowe, Aurora Guxholli, Andrew Livingstone, Jolanda Jetten, S. Reicher

Bibliographic record

VenueEuropean Journal of Social Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
FundersUniversity of St AndrewsTrent UniversityNottingham Trent University
KeywordsEthnographyEconomic JusticeHuman rightsTerm (time)PsychologySocial psychologySocial justiceCriminologySociologyPolitical scienceLawAnthropology

Abstract

fetched live from OpenAlex

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.088
GPT teacher head0.410
Teacher spread0.323 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2024
Admission routes1
Has abstractyes

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