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Record W2150698824 · doi:10.1177/1741659015596111

Bridging or fostering social distance? An analysis of penal spectator comments on Canadian penal history museums

2015· article· en· W2150698824 on OpenAlexaffabout
Matthew Ferguson, Justin Piché, Kevin Walby

Bibliographic record

VenueCrime Media Culture An International Journal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Gender, and Advertising
Canadian institutionsUniversity of WinnipegUniversity of Ottawa
Fundersnot available
KeywordsImprisonmentVisitor patternMemorializationCriminologyPunishment (psychology)SociologyMedia studiesPolitical scienceLawSocial psychologyPsychology

Abstract

fetched live from OpenAlex

Penal history museums are among the sites where cultural meanings about prisoners and imprisonment are developed, communicated, and consumed. Little research has explored what visitors take from these encounters. Drawing on literature concerning new media communication and Brown’s (2009) work on penal spectatorship, we analyze visitor comments about their sojourns into Canadian penal history sites found on TripAdvisor, a global travel website. We delve into the diverse stories that tourists share about their encounters with representations of incarceration, which we have found address the following themes: the performance of on-site actors; perceived authenticity of experiences and emotions; the convenience of visiting museums; attitudes about imprisonment; and views of penal history. Our research suggests that visits to penal history museums in Canada seldom translate into humanizing conceptions of the criminalized and views that challenge punitiveness among visitors, at least online. We also highlight how new media communications shape the actions of penal history museum workers in ways that tend to reinforce memorialization practices that foster social distance between authors and recipients of punishment.

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)0.002
metaresearch head score (Gemma)0.013
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 score0.267
Threshold uncertainty score0.536

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0090.005
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.174
GPT teacher head0.389
Teacher spread0.215 · 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

Citations42
Published2015
Admission routes2
Has abstractyes

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