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Record W4393318177 · doi:10.1080/13552600.2024.2328046

Paraphilic offending: another privilege of White men?

2024· article· en· W4393318177 on OpenAlexaffabout
Nora M. Thompson, R. Karl Hanson

Bibliographic record

VenueJournal of Sexual Aggression · 2024
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsCarleton University
Fundersnot available
KeywordsPrivilege (computing)White privilegePsychologyCriminologyWhite (mutation)Poison controlHuman factors and ergonomicsSuicide preventionInjury preventionComputer securityMedical emergencySociologyMedicineRace (biology)Computer scienceGender studies

Abstract

fetched live from OpenAlex

People from historically oppressed groups are over-represented in many Western criminal justice systems. Some studies have found, however, that certain paraphilic sexual offences, such as child sexual abuse imagery and exhibitionism, are predominately committed by White men. This meta-analysis compared the ethnoracial distribution of perpetrators of paraphilic offences and of sexual aggression offences against adults in Canada, the United States, Australia, and New Zealand (k = 35; N > 20,000). White men were more likely than Indigenous, Black, and Latino men to commit paraphilic offences, OR = 2.3. Among men who committed sexual offences, White men were more likely to have had adverse childhood experiences (ACEs), OR = 1.6. This pattern indicates that more in-depth analysis may need to be done on how power and privilege influence how people act on their atypical sexual interests. These differences in ACEs may necessitate further research on how intimacy violations may contribute to some paraphilic sexual offences.

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)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.030
GPT teacher head0.333
Teacher spread0.303 · 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 designObservational
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

Citations2
Published2024
Admission routes2
Has abstractyes

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