Bibliographic record
Abstract
Adolescents engage in risky behaviors due to a lack of self-control; many among them stop after they mature. This behavioral theory, supported by neuroscientific evidence on late brain maturation, implies that an age-participation rate plot of any risky behavior should be hump-shaped. In this thesis, I introduce maturation to a standard panel model describing risky behaviors to serve two purposes: First, doing so corroborates the neuroscientific findings on maturation timing. Second, it allows the study of maturation's effects on adolescent risky behaviors. The key difficulty of introducing maturation in to a behavioral model is that in a behavioral data set, maturation is a latent time-varying characteristic, and also it correlates with the observables (age in particular); therefore, a standard panel data model cannot capture it. To solve this problem, I define maturation as one or more unobserved treatments, with both the treatment effect and timing being unknown and heterogeneous. Chapter 1 reports the empirical findings of an analysis using the basic specification of this augmented econometric model with one unobservable treatment. Specifically, the estimated maturation age distribution has a median of age 21 and is right-skewed. Maturation effects are much stronger than environmental effects, evidenced by the observation that adolescents mostly stop engaging in risky behaviors due to maturation rather than environmental changes. The estimated maturation effect for binge drinking can serve as a benchmark for the evaluation of existing adolescent policies. Chapter 2 develops the formal theory in a treatment effect framework with multiple unobserved treatments. The theoretical development starts from a simple single treatment effect model. I then extract one unobserved treatment --- maturation --- which is neither a common age effect nor an individual fixed effect, and prove its identification conditions. Then I consider the general case with multiple unobserved treatments. Chapter 3 studies the gender gap in risky behaviors. While the maturation timings of the males and females are close to each other, their maturation effects differ. The environmental differences between the two genders also diverge as adolescents age. Together, these two findings explain a diverging gender gap related to risky behaviors.
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".