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Record W3202067292 · doi:10.1093/bjc/azab102

This is how it Feels: Activating Lived Experience in the Penal Voluntary Sector

2021· article· en· W3202067292 on OpenAlexaff
Gillian Buck, Philippa Tomczak, Kaitlyn Quinn

Bibliographic record

VenueThe British Journal of Criminology · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Toronto
FundersUK Research and Innovation
KeywordsVoluntary sectorShameLived experienceCriminal justiceInclusion (mineral)CriminologyInequalityNothingPolitical scienceTertiary sector of the economyAgency (philosophy)SociologyPublic relationsPsychologyBusinessGender studiesLawSocial science

Abstract

fetched live from OpenAlex

Abstract Increasing calls for ‘nothing about us without us’ envision marginalized people as valuable and necessary contributors to policies and practices affecting them. In this paper, we examine what this type of inclusion feels like for criminalized people who share their lived experiences in penal voluntary sector organizations. Focus groups conducted in England and Scotland illustrated how this work was experienced as both safe, inclusionary and rewarding and exclusionary, shame-provoking and precarious. We highlight how these tensions of ‘user involvement’ impact criminalized individuals and compound wider inequalities within this sector. The individual, emotional and structural implications of activating lived experience, therefore, require careful consideration. We consider how the penal voluntary sector might more meaningfully and supportively engage criminalized individuals in service design and delivery. These considerations are significant for broader criminal justice and social service provision seeking to meaningfully involve those with lived experience.

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)0.008
metaresearch head score (Gemma)0.014
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 score0.014
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0140.037
Scholarly communication0.0110.007
Open science0.0010.018
Research integrity0.0030.005
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 head0.271
GPT teacher head0.434
Teacher spread0.163 · 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

Citations39
Published2021
Admission routes1
Has abstractyes

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