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The paedophile in popular culture

2017· book· en· W2752476667 on OpenAlexaff
Steven Kohm

Bibliographic record

VenueOxford University Press eBooks · 2017
Typebook
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsMainstreamRepresentation (politics)Cultural criminologyCriminologySociologyPopular culturePoliticsMedia studiesPolitical scienceLaw

Abstract

fetched live from OpenAlex

This essay examines fictional representations of sex crime, focusing on the construction of the paedophile in contemporary popular culture. Representations of sex crime and criminals in film and television have tended to mirror broader societal and social scientific assumptions about the nature of the crime, the consequences for victims, and appropriate reactions to offending behavior. Moreover, cinematic explorations of child sexual abuse can offer metaphorical sites to critique contemporary understandings of the causes, consequence, and reactions to the behavior. This essay situates the representation of sex crime and criminals within broader historical, cinematic, and criminological developments over the past century. The author argues that fictional representations of the paedophile constitute a type of popular criminology that can enrich and extend the boundaries of mainstream academic discourse and provide a complex understanding of the philosophical, moral, and cultural meanings of sexual offending at particular moments and places in history.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.009
Scholarly communication0.0070.005
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.031
GPT teacher head0.269
Teacher spread0.238 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2017
Admission routes1
Has abstractyes

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