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Record W2616532633 · doi:10.1057/978-1-137-56135-0_25

Punishment as Sublime Edutainment: “Horrid Spectacles” at the Prison Museum

2017· book-chapter· en· W2616532633 on OpenAlexaff
Laura Huey, Ryan Broll

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2017
Typebook-chapter
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsUniversity of GuelphWestern University
Fundersnot available
KeywordsSublimePunishment (psychology)PrisonPsychologyArtCriminologySociologyAestheticsSocial psychology

Abstract

fetched live from OpenAlex

It has been suggested that the seeking out of aestheticized forms of crime-related pain and suffering for pleasurable consumption is a postmodern phenomenon (Lennon and Foley 2004). And yet, as Michel Foucault (1977) reminds us in his particularly gruesome discussion of the treatment of Damiens the regicide, public executions during the eighteenth century were frequently the occasion for festivals at which the enterprising sold refreshments and souvenirs. Indeed, we can trace crime and punishment as staple elements of human entertainment to much earlier historical periods: in the plays of Greek antiquity and in a variety of premodern life and death spectacles featuring the agonies of offenders against the social order. What is different about the use of crime and punishment as entertainment for contemporary audiences from earlier premodern forms is that crime and its punishment are now, comparatively speaking, largely abstract entities, the spectacle of actual suffering being largely obscured from the audience’s view.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.009
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.001

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 head0.033
GPT teacher head0.289
Teacher spread0.256 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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