Waiting to Execute: An Optimal Stopping Model of Capital Punishment Stays
Bibliographic record
Abstract
Economists have made repeated efforts through both theoretical modeling and empirical testing to understand the deterrent effect of capital punishment. By and large, they have found a negative and statistically significant effect of capital punishment on the act of murder (that is, the death penalty deters murder). Ehrlich [1975] provides the first systematic analysis of the relationship between capital punishment and murder along with the first empirical test of the deterrence hypothesis concerning not only capital punishment but also other deterrent measures. His results suggest that on the average eight murder victims might have been saved as a result of one execution for the sample period 1933-67 in the United States. Although Ehrlich's work was criticized by scholars such as Waldo [1981] and Forst [1983], many subsequent studies, using independent time-series and cross-section data from the United States [Ehrlich, 1977; Layson, 1985; Cloninger, 1992; Ehrlich and Liu, 1999; Dezhbakhsh, et al. 2000], Canada [Layson, 1983] and the UK [Wolpin, 1978], have offered corroborating evidence consistent with the deterrence hypothesis.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".