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Record W2994735818 · doi:10.1080/00224499.2019.1705960

Challenging the Standard Model of Sexual Response: Evidence of a Variable Male Sexual Response Cycle

2019· article· en· W2994735818 on OpenAlexaff
Dean M. Busby, Nathan D. Leonhardt, Chelom E. Leavitt, Veronica Hanna‐Walker

Bibliographic record

VenueThe Journal of Sex Research · 2019
Typearticle
Languageen
FieldMedicine
TopicSexual function and dysfunction studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologySexual desireSexual arousalArousalDevelopmental psychologyDemographicsSexual behaviorClinical psychologyDemographyHuman sexualitySocial psychology

Abstract

fetched live from OpenAlex

Historically the male sexual response cycle was seen as uniform and used as the standard for women. Recent research has suggested that men's sexual response cycle may vary more than previously thought. We asked 520 sexually active men between the ages of 18-73 to report on their sexual desire and arousal patterns during their last sexual experience. Using a latent class mixture model from retrospective sexual response data, we found five classes of desire and arousal patterns. These patterns were examined for associations with demographics, outcomes during the sexual experience, and outcomes for the global relationship. The experiences of arousal and desire appear to be indistinguishable for men in this sample. The Fluctuation sexual response class (19% of men) and the High sexual response class (40%) were significantly different from most of the other classes in duration of their sexual experiences and overall satisfaction with their sexual experiences. Still, most sexual response patterns were associated with healthy relational and sexual outcomes. Variability in the male sexual response cycle is important to acknowledge and normalize.

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)0.032
metaresearch head score (Gemma)0.110
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 score0.032
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.110
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.185
GPT teacher head0.423
Teacher spread0.238 · 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

Citations19
Published2019
Admission routes1
Has abstractyes

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