MétaCan
Menu
Back to cohort
Record W2601428893 · doi:10.7202/1092588ar

Punishment, blame and stigma after conflict: The experience of politically motivated former prisoners in Northern Ireland

2022· article· en· W2601428893 on OpenAlexvenueno aff
Ruth Jamieson

Bibliographic record

VenueCriminologie · 2022
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
Fundersnot available
KeywordsBlamePoliticsCriminologyPunishment (psychology)Stigma (botany)Social psychologyAttributionPolitical scienceEconomic JusticeSocial exclusionSociologyPsychologyLaw

Abstract

fetched live from OpenAlex

This paper examines the relationship between the politics of blame in post-conflict Northern Ireland and the treatment of politically motivated former prisoners. Using the examples of direct and indirect discrimination in the areas of employment and access to mental health services, the paper considers how the discursive operation of blaming produces evasions and attributions of guilt. It argues that such blaming practices have very real material consequences for the allocation or withholding of goods and burdens in the community. The paper notes also that the "cause of victims" is often appropriated by the press and other political actors for their own purposes, frequently to block the provision of public goods to one particular group of ex-combatants: ex-politically motivated prisoners. It concludes by posing a series of questions about blaming, justice and the moral authority of the victim in a transitional justice context. The claim of the paper is simply to offer some starting points for understanding the relationship between processes of blame, stigma and social exclusion.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0250.018
Scholarly communication0.0080.004
Open science0.0020.011
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0040.001

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 head0.146
GPT teacher head0.374
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueCriminologieSame topicMigration, Health and TraumaFrench-language works237,207