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Record W2018973804 · doi:10.1037/a0012069

Me, myself, and us: Salient self-threats and relational connections.

2008· article· en· W2018973804 on OpenAlexafffund
Christopher T. Burris, John K. Rempel

Bibliographic record

VenueJournal of Personality and Social Psychology · 2008
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsSt. Jerome's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologySalience (neuroscience)Social psychologySalientSelfHuman physical appearanceDevelopmental psychologyCognitive psychology

Abstract

fetched live from OpenAlex

Guided by their own amoebic self theory (C. T. Burris & J. K. Rempel, 2004), in 6 studies the authors explore the impact that involvement in an intimate relationship has on how a person appraises and responds to threat. They first show that people in relationship feel less constrained by their physical bodies compared with single people. In 3 subsequent studies involving physical size, blood/body donation, sexual activities, and responses to evil, they show that generalized sensitivity to bodily threat predicts self-protective reactions to specific physical threats among singles, but not among people in relationship, suggesting that intimate relationship involvement decreases the salience of the physical body. In the final pair of studies, they show that the salience of the physical body rebounds when people in relationship are primed, either subliminally or supraliminally, to think of themselves as distinct and separate from their partners. Thus, the present research shows how conceptualizing the self as "us" rather than "me" can transform an individual's response to the outside world, and highlights how physical cues in particular are affected by this process.

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)0.006
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.004
Scholarly communication0.0030.003
Open science0.0000.004
Research integrity0.0010.002
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 head0.062
GPT teacher head0.362
Teacher spread0.300 · 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

Citations18
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Personality and Social PsychologySame topicDeath Anxiety and Social ExclusionFrench-language works237,207