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Record W2044758498 · doi:10.1037/a0020310

The regulatory function of self-esteem: Testing the epistemic and acceptance signaling systems.

2010· review· en· W2044758498 on OpenAlexafffund
Danu Anthony Stinson, Christine Logel, John G. Holmes, Joanne V. Wood, Amanda L. Forest, Danielle Gaucher, Gráinne M. Fitzsimons, Jennifer Kath

Bibliographic record

VenueJournal of Personality and Social Psychology · 2010
Typereview
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of WaterlooUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyFunction (biology)Social psychologyValue (mathematics)Self-esteemCertaintyTask (project management)CognitionEpistemologyComputer science

Abstract

fetched live from OpenAlex

The authors draw on sociometer theory (e.g., Leary, 2004) and self-verification theory (e.g., Swann, 1997) to propose an expanded model of the regulatory function of self-esteem. The model suggests that people not only possess an acceptance signaling system that indicates whether relational value is high or low but also possess an epistemic signaling system that indicates whether social feedback is consistent or inconsistent with chronic perceived relational value (i.e., global self-esteem). One correlational study and 5 experiments, with diverse operationalizations of social feedback, demonstrated that the epistemic signaling system responds to self-esteem consistent or inconsistent relational-value feedback with increases or deceases in epistemic certainty. Moreover, Studies 3-6 demonstrated that the acceptance and epistemic signaling systems respond uniquely to social feedback. Finally, Studies 5 and 6 provide evidence that the epistemic signaling system is part of a broader self-regulatory system: Self-esteem inconsistent feedback caused cognitive efforts to decrease the discrepancy between self-views and feedback and caused depleted self-regulatory capacity on a subsequent self-control task.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.200
GPT teacher head0.457
Teacher spread0.257 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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

Citations88
Published2010
Admission routes2
Has abstractyes

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