Effects of Different Combinations of Intensity Categories on Self-Reported Exercise
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 Self-reports of exercise are used extensively in behavioral, social psychological, and epidemiological research (Ainsworth, Montoye, & Leon, 1994; Caspersen, 1997). Schwarz (1999) noted that many characteristics strongly influence self-reports of behavior, including question wording, format, and context. Of particular interest in the present study is the possible effect of providing different combinations of intensity categories (i.e., light/mild, moderate, and vigorous/strenuous) on self-reported exercise. A review of the exercise measurement literature indicates that researcher-developed and published questionnaires have varied in the number of exercise intensity categories they present to respondents. For example, researcher-developed questionnaires have often used only one category of exercise intensity, such as moderate (e.g., Miller, Trost, & Brown, 2002; Wallace, Buckworth, Kirby, & Sherman, 2000) or vigorous (e.g., Owen, Sedgwick, & Davies, 1988; Washburn, Goldfield, Smith, & McKinlay, 1990). Conversely, published questionnaires have typically used multiple intensity categories, such as moderate and vigorous/strenuous (e.g., Blair et al., 1985; Heath, Pate, & Pratt, 1993) or light/mild, moderate, and vigorous/strenuous (e.g., Baecke, Burema, & Frijters 1982; Godin & Shephard, 1985; Myers, Bader, Madhavan, & Froelicher, 2001). It is unknown, however, if providing different combinations of exercise intensity categories has any effect on the amount of exercise reported in a given intensity category or in total.
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) | 0.000 | 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