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Record W2899076970 · doi:10.1521/jscp.2018.37.9.697

Comparison of Team and Participant Ratings of Event Dependence: Inferential Style, Cognitive Style, and Stress Generation

2018· article· en· W2899076970 on OpenAlexaff
Laura M. Scallion, Jorden A. Cummings

Bibliographic record

VenueJournal of Social and Clinical Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPsychologyStyle (visual arts)Cognitive styleCognitionConsistency (knowledge bases)Social psychologyObserver (physics)Stress (linguistics)InferenceCognitive psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Introduction: Previous research has linked negative cognitive styles with stress generation. However, measures of cognitive styles have replied on ratings for hypothetical events, not experienced events. We examined the relationship between stress generation and attributional style for experienced events (i.e., inferential style) at both macro and daily levels. Methods: We measured stress generation in college students using the traditional objective team ratings (i.e., observer) as well as via participants’ own ratings (i.e., actor), which we argue captures more information and is consistent with calls for participant-centered research. Results: Cognitive style and inferential style positively correlated, indicating consistency between these two forms of assessment. Actor and observer identified events were significantly correlated for both dependent and independent events, suggesting that participants and teams are consistent in these categorizations. Results from both studies showed that inferential style was associated with actor but not observer identified dependent events. Discussion: Our findings provide some of the first evidence for the role of inferential style in actor identified stress generation and indicate that it is useful to examine both participant and observer ratings of stressful life events.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.331
GPT teacher head0.573
Teacher spread0.242 · 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 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

Citations4
Published2018
Admission routes1
Has abstractyes

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