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Record W2189022651 · doi:10.65030/idr.10018

Is Self-Monitoring Related to Social Comparison? It Depends How You Ask

2012· article· en· W2189022651 on OpenAlexaff
Alexander Soibel, Katrina Fong, Justin B. Mullin, Gwen Jenkins, Raymond A. Mar

Bibliographic record

VenueIndividual Differences Research · 2012
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsYork University
Fundersnot available
KeywordsSelf-monitoringConstruct (python library)Social psychologyAsk priceScale (ratio)Relation (database)PsychologySocial comparison theoryComputer scienceData miningGeography

Abstract

fetched live from OpenAlex

Self-monitoring and social comparison are theoretically related, as the former involves searching the environment for information to inform changes in one's own behavior and the latter involves seeking out social information for the purposes of comparison. We examined the relation between these two constructs, while simultaneously examining how scale choice for self-monitoring can greatly influence one's results and possible conclusions. Self-monitoring measures differed in their relation with social comparison based on their theoretical background. Moreover, self-monitoring measures related to social comparison exhibited diverging relations with the two major aspects of social comparison. These results illuminate our understanding of self-monitoring and social comparison, and the psychometric distinction between construct and measurement.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.464
GPT teacher head0.566
Teacher spread0.103 · 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; both teacher heads agree on what is shown here.

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

Citations10
Published2012
Admission routes1
Has abstractyes

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