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Record W2394846680 · doi:10.1177/1948550609358114

Don’t You Know How Much I Need You?

2010· article· en· W2394846680 on OpenAlexafffund
Jessica J. Cameron, Kelley J. Robinson

Bibliographic record

VenueSocial Psychological and Personality Science · 2010
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Manitoba
KeywordsPsychologySocial psychologySelf-esteemPerceptionAffect (linguistics)Test (biology)SeekersPsychological interventionDevelopmental psychologyCommunication

Abstract

fetched live from OpenAlex

Signal amplification bias, the tendency to overestimate how much one’s behavior conveys internal states, has been theorized to negatively affect relationships. The present study is the first to test whether signal amplification has negative consequences in close relationships and whether this form of miscommunication is more detrimental to lower self-esteem individuals, who doubt their partner’s regard. Dating couples participated in a lab-induced social support interaction. Results supported predictions, revealing that when lower self-esteem support seekers overestimated how much they conveyed, they rated their partners' responses as less supportive than higher self-esteem support seekers who also engaged in signal amplification. Yet self-esteem did not predict perceptions of a partner’s supportiveness when impressions exceeded metaperceptions. These results highlight the role of self-esteem in understanding implications for miscommunication and for targeting interventions.

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)0.002
metaresearch head score (Gemma)0.014
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.057
GPT teacher head0.433
Teacher spread0.376 · 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

Citations9
Published2010
Admission routes2
Has abstractyes

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