Disparities in Free Time Inactivity in the United States: Trends and Explanations
Bibliographic record
Abstract
What accounts for trends in socioeconomic disparities in physical activity during free time? Results from four national time-use studies between 1965 and 1999 suggest that there are increasing socioeconomic disparities in passive but not active free-time activities. The author evaluates several explanations for these trends. First, the least educated adults had more free time in 1999 than in 1965, and they spend nearly all this extra free time in home settings where the most common passive activities occur (e.g., television viewing). Second, less educated adults had less income per minute of free time in 1999 than in 1965, a trend that combines with increasing supply and reduced price of important passive choices to create economic incentives for passive activity. Third, the difference between low and high educated adults in the mediation of children's viewing habits has increased, an indication of rising socioeconomic disparities in tastes and stigma for this passive activity choice. Finally, historical data suggest that these changes in the use of free time are not simply free market outcomes but also consequences of political decisions favoring television infrastructure, auto-dependent built environments, and disinvestment in public recreation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".