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Record W2756478760 · doi:10.1177/0265407517729567

Nothing ventured, nothing gained: People anticipate more regret from missed romantic opportunities than from rejection

2017· article· en· W2756478760 on OpenAlexafffund
Samantha Joel, Jason E. Plaks, Geoff MacDonald

Bibliographic record

VenueJournal of Social and Personal Relationships · 2017
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Utah
KeywordsRomanceRegretPsychologyNothingRecallSocial psychologyContext (archaeology)AnxietyDevelopmental psychologyCognitive psychologyPsychoanalysisEpistemology

Abstract

fetched live from OpenAlex

Romantic pursuit decisions often require a person to risk one of the two errors: pursuing a romantic target when interest is not reciprocated (resulting in rejection) or failing to pursue a romantic target when interest is reciprocated (resulting in a missed romantic opportunity). In the present research, we examined how strongly people wish to avoid these two competing negative outcomes. When asked to recall a regrettable dating experience, participants were more than three times as likely to recall a missed opportunity rather than a rejection (Study 1). When presented with romantic pursuit dilemmas, participants perceived missed opportunities to be more regrettable than rejection (Studies 2–4), partially because they perceived missed opportunities to be more consequential to their lives (Studies 3 and 4). Participants were also more willing to risk rejection rather than missed romantic opportunities in the context of imagined (Study 4) and actual (Study 5) pursuit decisions. These effects generally extended even to less secure individuals (low self-esteem, high attachment anxiety). Overall, these studies suggest that motivation to avoid missed romantic opportunities may help to explain how people overcome fears of rejection in the pursuit of potential romantic partners.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.786
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.177
GPT teacher head0.354
Teacher spread0.177 · 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.

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

Citations23
Published2017
Admission routes2
Has abstractyes

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