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Record W2020191227 · doi:10.1037/0022-3514.88.4.632

Self-Regulation and Self-Presentation: Regulatory Resource Depletion Impairs Impression Management and Effortful Self-Presentation Depletes Regulatory Resources.

2005· article· en· W2020191227 on OpenAlexaff
Kathleen D. Vohs, Roy F. Baumeister, Natalie J. Ciarocco

Bibliographic record

VenueJournal of Personality and Social Psychology · 2005
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Mental Health
KeywordsPsychologyImpression managementPresentation (obstetrics)Interpersonal communicationSocial psychologyEgo depletionNormativeSelf-controlRegulatory focus theorySelf-disclosureSelf-enhancementImpression formationSelf-conceptPerceptionSocial perceptionNeuroscienceCreativity

Abstract

fetched live from OpenAlex

Self-presentation may require self-regulation, especially when familiar or dispositional tendencies must be overridden in service of the desired impression. Studies 1-4 showed that self-presentation under challenging conditions or according to counter-normative patterns (presenting oneself modestly to strangers, boastfully to friends, contrary to gender norms, to a skeptical audience, or while being a racial token) led to impaired self-regulation later, suggesting that those self-presentations depleted self-regulatory resources. When self-presentation conformed to familiar, normative, or dispositional patterns, self-regulation was less implicated. Studies 5-8 showed that when resources for self-regulation had been depleted by prior acts of self-control, self-presentation drifted toward less-effective patterns (talking too much, overly or insufficiently intimate disclosures, or egotistical arrogance). Thus, inner processes may serve interpersonal functions, although optimal interpersonal activity exacts a short-term cost.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.034
GPT teacher head0.373
Teacher spread0.339 · 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 designBench or experimental
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

Citations854
Published2005
Admission routes1
Has abstractyes

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