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Record W2332595811 · doi:10.1177/1468797615579564

Staged authenticity in penal history sites across Canada

2015· article· en· W2332595811 on OpenAlexaffabout
Kevin Walby, Justin Piché

Bibliographic record

VenueTourist Studies · 2015
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsUniversity of OttawaUniversity of Winnipeg
Fundersnot available
KeywordsImprisonmentNarrativeTourismHeritage tourismState (computer science)HistorySociologyExistentialismLawCriminologyAestheticsArchaeologyPolitical scienceArtLiterature

Abstract

fetched live from OpenAlex

Literature on penal tourism has examined meanings of imprisonment and punishment communicated in infamous American (e.g. Alcatraz, Eastern State Penitentiary), Australian (e.g. Fannie Bay Gaol, Port Arthur) and South African (e.g. Robben Island) sites. Yet such research has not extensively drawn from academic debates on authenticity and heritage that have become prominent in tourism research. This article examines the staging of authenticity in lesser-known penal history museums located across Canada. Whether large and aesthetically impressive or small and dingy, the existence of museums in original heritage penal sites raises questions about authenticity and how it is staged. Based on an analysis of field notes and interviews from visits to 45 penal history sites in Canada, we present four strategies (preservation, restoration, importation and creation) used by staff and volunteers at penal history museums to draw attention to four types of authenticity (architectural and spatial, tactile and visual, existential and narrative) that reinforce claims about the purported realities of incarceration found therein.

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 score0.078
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0150.008
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.140
GPT teacher head0.366
Teacher spread0.226 · 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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