Bibliographic record
Abstract
Notes: Center Discussion Papers are preliminary materials circulated to stimulate discussions and critical comments. The authors wish to thank the Florida Department of Juvenile Justice (DJJ) and the Justice Research Center (JRC), Inc. for providing the data used in this study. In particular, we wish to thank Sherry Jackson, Dr. Steven Chapman, and Ted Tollett from the Florida DJJ and Julia Blankenship and Dr. Kristin Winokur from the JRC for many helpful conversations concerning the data and, more generally, concerning the operation of the DJJ. We are also grateful to Joe Altonji, Tracy Falba, Erik Hjalmarsson, Mireira Jofre-Bonet, Carolyn Moehling, Paul Schultz, Ann Stevens, Chris Timmins, Chris Udry, and seminar participants at Yale University for their valuable comments and suggestions. This paper can be downloaded without charge from the Social Science Research Network electronic
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.115 | 0.013 |
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".