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Record W2014105289 · doi:10.3138/cjhs.2592

Why have sex? Reasons for having sex among lesbian, bisexual, queer, and questioning women in romantic relationships

2014· article· en· W2014105289 on OpenAlexaffvenue
Jessica Wood, Robin R. Milhausen, Nicole Jeffrey

Bibliographic record

VenueThe Canadian Journal of Human Sexuality · 2014
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPsychologyLesbianPleasureRomanceQueerDevelopmental psychologyHeterosexualityPsychosocialSocial psychologyGender studiesHomosexualitySociology

Abstract

fetched live from OpenAlex

Research has traditionally cited pleasure and reproduction as the primary reasons to engage in sex. However, recent research suggests that there are many psychosocial reasons that women engage in sex and that relational factors such as relationship duration may also influence why women engage in sex. Few studies have examined reasons for sex among sexual minority women, although research has suggested that reasons may be similar to and different from those of heterosexual women. Using the YSEX? survey measure, the current study examined reasons for having sex among 229 lesbian, bisexual, queer and questioning women, aged 18–59 currently in a romantic relationship. The most frequent reasons women reported for engaging in sex were reasons related to pleasure and love/commitment. Contrary to theories of love and attachment, women in the current study did not report significantly different reasons for engaging in sex depending upon the duration of the relationship. Women in earlier stages of their relationship were just as likely to report engaging in sex to feel close to their partner, as were women in later stages of their relationship. In addition, women in later stages of their relationship were just as likely to report engaging in sex out of a physical desire for their partner as were women in earlier stages of their relationship. The strengths and limitations of the study, along with implications of the results 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)0.007
metaresearch head score (Gemma)0.001
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 score0.562
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.087
GPT teacher head0.352
Teacher spread0.266 · 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

Citations32
Published2014
Admission routes2
Has abstractyes

Explore more

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