Capital Punishment and Deterrence: Understanding Disparate Results
Bibliographic record
Abstract
Objectives: Investigate how different model assumptions have driven the conflicting findings in the literature on the deterrence effect of capital punishment. Methods: The deterrence effect of capital punishment is estimated across different models that reflect the following sources of model uncertainty: 1) the uncertainty about the probability model generating the aggregate murder rate equation, 2) the uncertainty about the determinants of individual's choice of committing a murder or not, 3) the uncertainty about state level heterogeneity, and 4) the uncertainty about the exchangeability between observations with zero murder case and those with positive murder cases. Results: First, the estimated deterrence effects exhibit great dispersion across models. Second, a particular subset of models -linear models with constant coefficients - always predict a positive deterrence effect. All other models predict negative deterrence effects. Third, the magnitudes of the point estimates of deterrence effect differ mainly because of the choice of linear versus logistic specifications. Conclusions: The question about the deterrence effect of capital cannot be answered independently from substantive assumptions on what determines individual behavior. The need for judgment cannot be escaped in empirical work.
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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 | 0.022 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 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".