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Record W2582982588 · doi:10.1177/1079063216686119

Assessing Sexual Interest in Children Using the Go/No-Go Association Test

2017· article· en· W2582982588 on OpenAlexaff
Ross M. Bartels, Anthony R. Beech, Leigh Harkins, David Thornton

Bibliographic record

VenueSexual Abuse · 2017
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsGo/no goTest (biology)Association (psychology)PsychologyComputer scienceBiologyPsychotherapist

Abstract

fetched live from OpenAlex

The present study investigated whether a latency-based Go/No-Go Association Task (GNAT) could be used as an indirect measure of sexual interest in children. A sample of 29 individuals with a history of exclusive extrafamilial offenses against a child and 15 individuals with either a history of exclusive intrafamilial or mixed offenses (i.e., against both adults and children) were recruited from a treatment center in the United States. Also, a sample of 26 nonoffenders was recruited from a university in the United Kingdom. All participants completed the Sexual Fantasy-GNAT, a Control-GNAT, and two self-report measures of sexual fantasy. It was hypothesized that, relative to the two comparison groups, the extrafamilial group would respond faster on the block that paired "sexual fantasy" and "children." Also, GNAT scores were expected to correlate with child-related sexual fantasies. Support was found for both hypotheses. Response-latency indices were also found to effectively distinguish the extrafamilial group, as well as those who self-reported using child-related sexual fantasies. The implications of these findings, along with the study's limitations and suggestions for future research, are discussed.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.083
GPT teacher head0.370
Teacher spread0.287 · 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.

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

Citations15
Published2017
Admission routes1
Has abstractyes

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