Identifying Sibling Influence on Teenage Substance Use
Bibliographic record
Abstract
A number of studies have found substantial correlations in risky behavior between siblings, raising the possibility that adolescents may directly influence the actions of their brothers or sisters.We assess the extent to which correlations in substance use and selling drugs are due to causal effects.Our identification strategy relies on panel data, the fact that the future does not cause the past, and the assumption that the direction of influence is from older siblings to younger siblings.Under this assumption along with other restrictions on dynamics, one can identify the causal effect from a regression of the behavior of the younger sibling on the past behavior and the future behavior of the older sibling.We also estimate a joint dynamic model of the behavior of older and younger siblings that allows for family specific effects, individual specific heterogeneity, and state dependence.We use the model to simulate the dynamic response of substance use to the behavior of the older sibling.Our results suggest that smoking, drinking, and marijuana use are affected by the example of older siblings, but most of the link between siblings arises from common influences.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".