Toward an Explanation of Observed Ethnic Differences in Youths' Tobacco Use
Bibliographic record
Abstract
We examined whether differences in smoking rates of Asian and white/Caucasian youth could be explained by personal (gender, employment status, volunteerism, parental education, and income) and social factors including differences in youths' relationships with their parents, extent of enculturation, and exposure to parental or peer smoking. A survey was conducted of a random sample of schools in 2 cities in British Columbia, Canada to obtain data from 3,278 high school students. Results from logistic regression analysis indicated smoking status was explained by place of birth, volunteerism, amount of income received from parents and employers, characteristics of the parental-child relationship, and parental and peer smoking status. Differences in the estimated risks of smoking of Asian youth and white youth were moderated by the youths' willingness to tell their parents about their lives, whether they worked for pay, and whether the Asian youth spoke English at home.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".