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Record W2067414703 · doi:10.1177/1462474511414784

The polysemy of punishment memorialization: Dark tourism and Ontario's penal history museums

2011· article· en· W2067414703 on OpenAlexaffabout
Kevin Walby, Justin Piché

Bibliographic record

VenuePunishment & Society · 2011
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsMemorial University of NewfoundlandUniversity of Victoria
Fundersnot available
KeywordsMemorializationImprisonmentPunitive damagesPunishment (psychology)CriminologyNarrativeSociologyMeaning (existential)PolysemyLawPolitical scienceArtLiteraturePsychologySocial psychology

Abstract

fetched live from OpenAlex

Contributing to debates about cultural representations of prisons and prisoners, as well as exploring the crossover between the dark tourism literature and cultural criminology, this article reflects on how penal museums in the province of Ontario, Canada, create and communicate meaning as it regards imprisonment and punishment. Drawing from field notes made after observations at penal museums located in central and eastern Ontario cities and towns, we contend that penal museum relics offer a polysemy of meaning to viewers, as critical, indifferent and punitive interpretations are possible. Based on analysis of tour guide narratives as well as penal relics, we explore how the process of memorialization in many of these museums is organized around the idea of penal reform, which positions imprisonment and punishment as remnants of the past and introduces a social distance between the punished and the penal spectator.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.350
Threshold uncertainty score0.705

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0210.021
Scholarly communication0.0060.003
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.048
GPT teacher head0.263
Teacher spread0.216 · 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

Citations111
Published2011
Admission routes2
Has abstractyes

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