Measuring Respiratory Health in Longitudinal Social Science Surveys
Bibliographic record
Abstract
Objectively assessing respiratory health in longitudinal social science surveys would involve collecting pulmonary function measures on research participants, either in clinic settings or at home. These measures include indicators of volume (e.g., maximal amount of air blown in the first second of a forced exhalation) and airflow (maximal speed air is exhaled during a forced exhalation). Equipment options include office spirometry, portable spirometry, or home peak flow monitoring. Each option has different equipment and personnel costs. The types of research questions that could be answered using pulmonary function measures in longitudinal household surveys are quite broad, ranging from effects of socioeconomic status and race/ethnicity on respiratory health to social/environmental factors that contribute to respiratory health to the long-term social and economic consequences of respiratory health problems. Currently, such data are lacking. Given the potential payoffs in scientific knowledge, adding these measures to population-based surveys merits serious consideration.
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How this classification was reachedexpand
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".