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
OBJECTIVE: To examine whether--and why--people underestimate how much they enjoy exercise. DESIGN: Across four studies, 279 adults predicted how much they would enjoy exercising, or reported their actual feelings after exercising. MAIN OUTCOME MEASURES: Main outcome measures were predicted and actual enjoyment ratings of exercise routines, as well as intention to exercise. RESULTS: Participants significantly underestimated how much they would enjoy exercising; this affective forecasting bias emerged consistently for group and individual exercise, and moderate and challenging workouts spanning a wide range of forms, from yoga and Pilates to aerobic exercise and weight training (Studies 1 and 2). We argue that this bias stems largely from forecasting myopia, whereby people place disproportionate weight on the beginning of a workout, which is typically unpleasant. We demonstrate that forecasting myopia can be harnessed (Study 3) or overcome (Study 4), thereby increasing expected enjoyment of exercise. Finally, Study 4 provides evidence for a mediational model, in which improving people's expected enjoyment of exercise leads to increased intention to exercise. CONCLUSION: People underestimate how much they enjoy exercise because of a myopic focus on the unpleasant beginning of exercise, but this tendency can be harnessed or overcome, potentially increasing intention to exercise.
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) | 0.003 | 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 it