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Record W2523723528 · doi:10.3138/cjhs.253-a3

The mediating role of technology and the prevalence of sexual sensation seeking behaviours among men of varying sexual orientations in Canada, the United States, and Western Europe

2016· article· en· W2523723528 on OpenAlexaffvenueabout
Raymond M. McKie, Drake Levere, Terry P. Humphreys

Bibliographic record

VenueThe Canadian Journal of Human Sexuality · 2016
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsTrent UniversityUniversity of British ColumbiaWilfrid Laurier University
Fundersnot available
KeywordsSensation seekingCasualSexual orientationDemographyMen who have sex with menPsychologySensationMedicineSocial psychologyPolitical scienceSociologyPersonalityFamily medicine

Abstract

fetched live from OpenAlex

Technology is being readily incorporated into the sexual lives of men. We investigated the relationship between sexual sensation seeking and technology use for sex purposes of heterosexual men and men who have sex with men (MSM) from different geographic locations. A total of 317 MSM and 276 heterosexual men completed the survey. Participants were recruited from Canada (n=230), the United States (n=205), and Western Europe (n=113). No relationship was found between sexual orientation and sexual sensation seeking. Casual relationships between sexual sensation seeking and technology use based on sexual orientation were explored in two mediation models. Canadian men scored higher in sexual sensation seeking than men from Western Europe. There were no other significant differences across geographic locations. Implications of technology use and sexual sensation seeking 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 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)0.001
metaresearch head score (Gemma)0.003
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: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.293
Teacher spread0.269 · 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
Published2016
Admission routes3
Has abstractyes

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