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Record W2096733767 · doi:10.1177/0146167212445790

Perceived Regard Explains Self- Esteem Differences in Expressivity

2012· article· en· W2096733767 on OpenAlexaff
Danielle Gaucher, Joanne V. Wood, Danu Anthony Stinson, Amanda L. Forest, John G. Holmes, Christine Logel

Bibliographic record

VenuePersonality and Social Psychology Bulletin · 2012
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversity of VictoriaUniversity of WaterlooUniversity of Winnipeg
Fundersnot available
KeywordsPsychologyCognitive reframingSelf-esteemFeelingSocial psychologyPerceptionPresentational and representational actingPsychological interventionExpressive SuppressionTask (project management)Developmental psychologyCognitionCognitive reappraisal

Abstract

fetched live from OpenAlex

Baumeister, Tice, and Hutton proposed that individuals with low self-esteem (LSEs) adopt a more cautious, self-protective self-presentational style than individuals with high self-esteem (HSEs). The authors predicted that LSEs' self-protectiveness leads them to be less expressive--less revealing of their thoughts and feelings--with others than HSEs, and that this self-esteem difference is mediated by their perceptions of the interaction partner's regard for them. Two correlational studies supported these predictions (Studies 1 and 2). Moreover, LSEs became more expressive when their perceived regard was experimentally heightened--when they imagined speaking to someone who was unconditionally accepting rather than judgmental (Study 3) and when their perceptions of regard were increased through Marigold, Holmes, and Ross's compliment-reframing task (Study 4). These findings suggest that LSEs' expressiveness can be heightened through interventions that reduce their concerns about social acceptance.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.059
GPT teacher head0.393
Teacher spread0.334 · 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

Citations68
Published2012
Admission routes1
Has abstractyes

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