Estimating frequencies of emotions and actions: a web‐based diary study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it