Bibliographic record
Abstract
This thesis examines decision-making about risks under conditions of uncertainty. Research specifically studies adolescents and smoking to uncover which information sources play influential roles in forming smoking risk perceptions. Findings aim to offer policy-related, theoretical and methodological meaning This study looks at two key issues. First, it links risk perceptions to smoking decisions to highlight the motivation for understanding the predictors of risk perceptions. Second, research investigates how individuals use information sources (ex. parents, doctors, peers, health warning messages) employing a Bayesian decision-making model. This thesis performs empirical analysis using The Canadian Youth Smoking Survey (2002) (19,018 respondents, 10-15 years) and The U.S. National Survey on Drug Use and Health (2004) (18,294 respondents, 12-17 years). Across both datasets, adolescents' risk perceptions and likelihood of having never smoking a cigarette were found to be positively related. However, smoking behaviors were never found to significantly predict risk perceptions once controlling for endogeneity between risk perceptions and behaviors. This suggests that adolescents rely on exogenous information sources about smoking risks rather than personal experiences to form perceptions of smoking risks. From a policy perspective, medical professionals talking with adolescent patients about smoking, parents' smoking, societal smoking prevalence (more than peers' smoking), awareness of tobacco package warning labels and knowledge of school smoking rules (but not the rules themselves) were found to predict adolescents' risk perceptions. From a theoretical perspective, this thesis alters the Bayesian model to include environmental and social effects. It also finds support for the role of affect heuristics in decision making involving risks. Findings also point to evidence of principal-agency relationships between medical professionals and adolescents. Analysis also highlights how spatial proximity impacts the credibility adolescents attach to behavioral examples and opinions regarding smoking. From a methodological standpoint, evidence suggests that adolescents' expressions of their assessment of risk depend upon elicitation methodology used and that work focusing on predictors of risk perceptions should include direct (ex. parents discussing risks) and indirect (ex. societal smoking prevalence) sources of information.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".