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Record W2519230810 · doi:10.1111/spc3.12261

Applying Theories of Communal Motivation to Sexuality

2016· article· en· W2519230810 on OpenAlexafffund
Amy Muise, Emily A. Impett

Bibliographic record

VenueSocial and Personality Psychology Compass · 2016
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversity of TorontoYork University
FundersUniversity of Guelph
KeywordsPsychologyHuman sexualityRomanceSocial psychologySexual behaviorSexual attractionSPARK (programming language)Quality (philosophy)Developmental psychologyGender studiesSociologyEpistemologyPsychoanalysisComputer science

Abstract

fetched live from OpenAlex

Abstract One important but challenging aspect of maintaining a satisfying romantic relationship is keeping the sexual spark alive. Research suggests the importance of a couple's sexual connection in the maintenance of their relationship, but sustaining high levels of desire for a partner over the course of time can be difficult. In the current review, we argue that one novel approach to understanding how couples might maintain desire and satisfaction over the course of time in their relationships is applying theories of communal motivation to the domain of sexuality. In this line of research, we have demonstrated that people high in sexual communal strength – those who are motivated to be non‐contingently responsive to their partners' sexual needs – are able to sustain higher sexual desire over the course of time and navigate sexual disagreements in a way that maintains both partners' relationship quality. Future research directions include broadening the view of sexual needs to include the need to decline or reject a partner's sexual advances and investigating how partners manage unmet sexual needs.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.004
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.448
Teacher spread0.361 · 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 designTheoretical or conceptual
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

Citations44
Published2016
Admission routes2
Has abstractyes

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