Identifying Sibling Influence on Teenage Substance Use
Bibliographic record
Abstract
Joseph G. Altonji, Sarah Cattan and Iain Ware Joseph G. Altonji is Thomas Dewitt Cuyler Professor of Economics at Yale University and a Research Associate at NBER. Sarah Cattan is a Senior Research Economist at the Institute for Fiscal Studies. Iain Ware is a Principal at Bain Capital. The authors are grateful to the referees, Jerome Adda, Monica Deza, Greg Duncan, Patrick Kline, Amanda Kowalski, Costas Meghir, Robert T. Michael, and participants in seminars at UC Berkeley, Brigham Young University, University of Chicago, The European Institute, MIT, New York University, the NLSY97 conference at the Bureau of Labor Statistics (May 2008), NBER Health 2009 Summer Institute, the Institute for Fiscal Studies, UC San Diego, the SOLE/EALE 2010 Meetings, Stanford University, the University of Toronto, and Yale University for valuable comments.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".