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Record W3166128194 · doi:10.1177/10693971211021816

Reasons of Singles for Being Single: Evidence from Brazil, China, Czech Republic, Greece, Hungary, India, Japan and the UK

2021· article· en· W3166128194 on OpenAlexaff
Menelaos Apostolou, Béla Birkás, Caio Santos Alves da Silva, Gianluca Esposito, Rafael Ming Chi Santos Hsu, Peter K. Jonason, Konstantinos Karamanidis, O Jiaqing, Yohsuke Ohtsubo, Ádám Putz, Daniel Sznycer, Andrew G. Thomas, Jaroslava Varella Valentová, Marco Antônio Corrêa Varella, Karel Kleisner, Jaroslav Flegr, Yan Wang

Bibliographic record

VenueCross-Cultural Research · 2021
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsFlirtingCzechChinaDemographyPsychologyGeographyDemographic economicsPolitical scienceSocial psychologySociologyEconomics

Abstract

fetched live from OpenAlex

The current research aimed to examine the reasons people are single, that is, not in an intimate relationship, across eight different countries—Brazil, China, Czech Republic, Greece, Hungary, India, Japan, and the UK. We asked a large cross-cultural sample of single participants ( N = 6,822) to rate 92 different possible reasons for being single. These reasons were classified into 12 factors, including one’s perceived inability to find the right partner, the perception that one is not good at flirting, and the desire to focus on one’s career. Significant sex and age effects were found for most factors. The extracted factors were further classified into three separate domains: Perceived poor capacity to attract mates, desiring the freedom of choice, and currently being in between relationships. The domain structure, the relative importance of each factor and domain, as well as sex and age effects were relatively consistent across countries. There were also important differences however, including the differing effect sizes of sex and age effects between countries.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.136
GPT teacher head0.478
Teacher spread0.342 · 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; both teacher heads agree on what is shown here.

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
Published2021
Admission routes1
Has abstractyes

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