Capturing the temporal dynamics of personality in daily life: The Personality and Contextualized Experiences (PACE) Study
Bibliographic record
Abstract
Thoughts, feelings, and behaviors constantly change in everyday life. Dynamic personality approaches emphasize that these short-term changes are not only relevant for understanding how enduring personality patterns arise but are themselves central to what personality is. Although the growing use of the experience sampling method (ESM) has made it more feasible to apply such approaches, methodological challenges limit the inferences that can be drawn about temporal personality dynamics. This paper describes the rationale, procedure, and dataset of the Personality and Contextualized Experiences (PACE) Study, a large, multi- site ESM study that comprises 1,908 undergraduates (130,402 observations) across Canada, Germany, and the United States. Of these, 714 participants completed the entire study (baseline survey, ≥ 100 ESM surveys, and follow-up survey). The PACE Study aims to advance the study of personality dynamics by combining a large sample with several methodological features targeting key design and measurement aspects for accurately capturing and interpreting these dynamics. The features encompass an experimental comparison of two sampling designs, retrospective assessments of missed states, and a personalized assessment of situational information. The study further includes a newly developed global self-report questionnaire intended to capture aspects of people’s dynamic patterns of states without requiring ESM data.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".