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Record W4280626197 · doi:10.1111/lasr.12603

“[Y]ou are better off talking to a f****** wall”: The perceptions and experiences of grievance procedures among incarcerated people in Ireland

2022· article· en· W4280626197 on OpenAlexaff
Sophie van der Valk, Eva Aizpurúa, Mary Rogan

Bibliographic record

VenueLaw & Society Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsTrinity College
Fundersnot available
KeywordsGrievancePerceptionPsychologyEconomic JusticeSocial psychologyLegal consciousnessConsciousnessSociologyPolitical scienceCriminologyLaw

Abstract

fetched live from OpenAlex

Abstract The ways in which grievance procedures are used and perceived by incarcerated people raise important questions about the operation of procedural justice and legal consciousness and mobilization scholarship in settings where rights are especially vulnerable. This paper analyzes perceptions and usage of the grievance procedure for incarcerated people using survey data from people ( N = 508) in three prisons in Ireland. We find that incarcerated people's views of the grievance procedures are generally negative, though some use it, especially those serving long sentences and those in segregation, with education level not significant in terms of usage. Additionally, having confidence in staff is associated with satisfaction with the procedure, as is the perception that one's rights are respected, showing important connections between perceptions of complaints and aspects of legal consciousness. We suggest a need for further situated analyses of procedural justice and legal consciousness, as well as practical requirements for complaints systems to elicit confidence among incarcerated people.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.943

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.014
GPT teacher head0.301
Teacher spread0.287 · 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 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

Citations11
Published2022
Admission routes1
Has abstractyes

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