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Record W2523551249 · doi:10.5127/jep.055116

Reactions to Prospective Positive vs. Negative Evaluation in the Laboratory: A Comparison of High and Low Socially Anxious Participants

2016· article· en· W2523551249 on OpenAlexafffund
Kevin C. Barber, David A. Moscovitch

Bibliographic record

VenueJournal of Experimental Psychopathology · 2016
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsUniversity of Waterloo
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsAnticipation (artificial intelligence)Social anxietyPsychologyFear of negative evaluationAnxietyContext (archaeology)Construct (python library)Extant taxonClinical psychologyTrait anxietyTraitDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

We present a study designed to investigate fear of positive vs. negative evaluation within the context of a laboratory-based paradigm designed to evoke social threat. Eighty-nine undergraduates with high (n = 43) or low (n = 46) levels of trait social anxiety took part in a “getting acquainted” task. Participants rated their anxiety about receiving prospective positive vs. negative evaluation in anticipation of receiving public feedback on a filmed introduction of themselves that they had made for an unknown social partner whom they expected they would later meet. Results demonstrated, in contrast to extant theories of fear of positive evaluation in social anxiety, that all participants, including those with high levels of social anxiety, rated the prospect of positive evaluation as anxiety reducing. This finding raises important questions about the construct of fear of positive evaluation and how to measure it “in vivo” in an ecologically valid manner.

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)0.004
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.049
GPT teacher head0.420
Teacher spread0.371 · 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

Citations14
Published2016
Admission routes2
Has abstractyes

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Same venueJournal of Experimental PsychopathologySame topicAnxiety, Depression, Psychometrics, Treatment, Cognitive ProcessesFrench-language works237,207