Estimating frequencies of emotions and actions: a web‐based diary study
Bibliographic record
Abstract
Abstract Mental health questionnaires often ask respondents to report how frequently they experience different emotions. We report two experiments designed to assess the accuracy of these reports and the strategies used to generate them. Each day for 2 weeks, participants in Experiment 1 filled out a web‐based emotions‐and‐activities checklist. Then, they estimated the diary‐period frequency of these emotions and activities and indicated how they generated each estimate. In Experiment 2, participants provided frequency estimates and strategy reports, but did not fill out the checklist. We found that (a) the frequency estimates were quite accurate for emotions and activities, (b) participants relied on memory‐based strategies (enumeration and direct retrieval) when estimating activity frequencies, but (c) used self‐knowledge strategies (personality beliefs and schematic inferences) somewhat more than memory strategies for emotions and (d) the relationship between strategy use and question type was unaffected by diary keeping. We conclude by considering practical and theoretical implications. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".