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Record W3189719813 · doi:10.1080/00224499.2021.1948957

Attraction to Physical and Psychological Features of Children in Child-Attracted Persons

2021· article· en· W3189719813 on OpenAlexaff
Frederica M. Martijn, Kelly M. Babchishin, Lesleigh E. Pullman, Kailey Roche, Michael C. Seto

Bibliographic record

VenueThe Journal of Sex Research · 2021
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsRoyal Ottawa Mental Health CentreUniversity of OttawaCarleton University
FundersVrije Universiteit Amsterdam
KeywordsAttractionPsychologySexual attractionDevelopmental psychologyPersonalityInterpersonal attractionSocial psychologySexual behavior

Abstract

fetched live from OpenAlex

In an online survey of 274 self-identified child-attracted persons (CAPs), we examined the attraction ratings given to sets of 9 physical and 12 psychological features of children, and asked CAPs to identify additional features that were not listed. We also examined the relationships between these attraction ratings and attraction to children dimensions (age mono-/polymorphism, exclusivity of attraction to children, and gender attraction), history of falling in love with a child, and detected sexual offending history. There was relatively little differentiation across physical features and psychological features; all averages were approximately 4 or higher on a 5-point scale. Attraction ratings were mostly weakly and inconsistently related to our other study variables. The exception was that CAPs who had fallen in love with a child rated 11 out of 12 psychological features as more attractive than CAPs who had not fallen in love with a child, with small to moderate effect sizes. These two groups did not differ in ratings for physical features. Our qualitative content analysis of participant-suggested features revealed six physical themes (inter alia, face and head, children's bodies) and five psychological themes (inter alia, personality, harmlessness) that were important to CAPs' attraction to children.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.143
GPT teacher head0.482
Teacher spread0.339 · 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 teacher head, 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

Citations17
Published2021
Admission routes1
Has abstractyes

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