Comparison of Team and Participant Ratings of Event Dependence: Inferential Style, Cognitive Style, and Stress Generation
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".