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Record W2891021109 · doi:10.1080/00224499.2018.1511965

Differences in Visual Attention Patterns to Sexually Mature and Immature Stimuli Between Heterosexual Sexual Offenders, Nonsexual Offenders, and Nonoffending Men

2018· article· en· W2891021109 on OpenAlexaff
Milena Vásquez-Amézquita, Juan David Leongómez, Michael C. Seto, Alicia Salvador

Bibliographic record

VenueThe Journal of Sex Research · 2018
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsPsychologyFixation (population genetics)AudiologyEye trackingFixation timeVisual attentionDevelopmental psychologyPopulationDemographyMedicinePsychiatryCognition

Abstract

fetched live from OpenAlex

Men, whether gynephilic or androphilic, show both early and late attention toward adults and not toward children. We examined early and late visual attention to sexually mature versus immature stimuli in four groups of heterosexual men: sexual offenders against children (SOAC = 18), sexual offenders against adults (SOAA = 16), nonsexual offenders (NSO = 18), and nonoffending men (NOM = 19). We simultaneously presented adult and child stimuli and measured time to first fixation, number of first fixations, total duration of fixation, and fixation count to four areas of interest: entire body, then face, chest, and pelvis. We found a significant interaction where only SOAC tended to fixate more first times to child than to adult stimuli. Conversely, we found longer total duration of fixations for the bodies of adults compared to the bodies of children in all groups; however, in both the total duration of fixations and the fixation count for the whole body, but especially in the chest, SOAC tended to fixate longer and more often on child stimuli than the other two groups of offenders, but not longer or more often than NOM. This study adds to the limited research using eye-tracking techniques in samples of SOAC.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
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 head0.150
GPT teacher head0.440
Teacher spread0.290 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

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