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Record W3089966949 · doi:10.1111/bjop.12475

Intended responses to romantic partners’ annoying behaviours vary with willpower beliefs

2020· article· en· W3089966949 on OpenAlexaff
Zoë Francis, Veronika Job

Bibliographic record

VenueBritish Journal of Psychology · 2020
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologySocial psychologyContext (archaeology)RomanceAffect (linguistics)Developmental psychologyControl (management)CommunicationPsychoanalysis

Abstract

fetched live from OpenAlex

When a romantic partner behaves in an annoying way - for example, by leaving a mess - we might respond with frustration or understanding. Responses may vary with contextual factors, including whether the partner could be mentally fatigued or depleted. We hypothesized that limited willpower theorists - who believe self-control diminishes with use - might be especially likely to consider their partner's preceding mental exertion. Two preregistered studies (combined N = 428) examined participants' responses to four hypothetical scenarios. Limited theorists responded more compassionately to infractions performed after fatiguing days than to those performed after relaxing days; non-limited theorists responded more consistently, regardless of context. Beliefs about one's own willpower, rather than beliefs about one's partner's willpower, can affect how people respond to their partner's undesirable behaviours.

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)0.005
metaresearch head score (Gemma)0.034
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 score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.100
GPT teacher head0.440
Teacher spread0.340 · 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
Published2020
Admission routes1
Has abstractyes

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