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Record W2542980221 · doi:10.5539/ass.v12n12p143

Impact of College Students’ Romantic Relationship Changes on Reorientation of Mate Selection Criteria

2016· article· en· W2542980221 on OpenAlexvenueno aff
Qisheng Zhan, Wenjie Chi, Kevin Guzman Iglesias, Xiaoran Yang, Han Wang, Jing Zhang, Chuanyun Lu, Miao Li

Bibliographic record

VenueAsian Social Science · 2016
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsnot available
FundersTianjin University
KeywordsRomanceAffectionPsychologySelection (genetic algorithm)PersonalitySocial psychologyAffect (linguistics)Mate choiceDevelopmental psychologyEcologyCommunication

Abstract

fetched live from OpenAlex

Students may experience two kinds of important romantic relationship changes during their transition from high school to higher education. One is a break-up between previously dating partners, and the other is the establishment of a romantic relationship between students who were single before. These two kinds of experiences also affect orientation of their mate selection criteria. This study takes samples from freshmen with different romantic relationship experiences. The freshmen’s dynamic assessment data towards mate selection criteria are obtained by means of investigative questionnaires related to mate selection criteria. Especially, reorientation to mate selection criteria is investigated after their romantic relationship changes. The results show that romantic relationship changes have a significant influence on assessment to the importance of mate selection criteria such as physiological requirements, morality, personality traits, inclusiveness, complementation, affection. Romantic relationship changes don’t have significant influence on assessment to the importance of physical and social conditions.

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)0.004
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.034
GPT teacher head0.461
Teacher spread0.427 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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