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
OBJECTIVES: This article examines determinants of self-perceived health. Factors associated with very good/excellent rather than good health are compared with those associated with fair/poor rather than good health. DATA SOURCE: The data are from the household cross-sectional and longitudinal components of the first three cycles (1994/95, 1996/97 and 1998/99) of Statistics Canada's National Population Health Survey (NPHS). ANALYTICAL TECHNIQUES: Cross-tabulations from the 1998/99 NPHS cross-sectional file were used to estimate the prevalence of very good/excellent and fair/poor health by sex and age group. Based on the longitudinal file, predictors of health perceptions in 1998/99 were studied in a multivariate model using generalized logistic regression. MAIN RESULTS: While physical conditions were strongly related to health perceptions, some lifestyle, socio-economic and psychosocial factors were also statistically significant. Heavy smoking, irregular exercise and overweight were associated with fair/poor health ratings. Unhealthy changes in lifestyle were associated with fair/poor rather than good health. Distress, low self-esteem and low socio-economic status were negatively associated with very good/excellent health.
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