Who Survives on Death Row? An Individual and Contextual Analysis
Bibliographic record
Abstract
What are the relationships between death row offender attributes, social arrangements, and executions? Partly because public officials control executions, theorists view this sanction as intrinsically political. Although the literature has focused on offender attributes that lead to death sentences, the post-sentencing stage is at least as important. States differ sharply in their willingness to execute and less than 10 percent of those given a death sentence are executed. To correct the resulting problems with censored data, this study uses a discrete-time event history analysis to detect the individual and state-level contextual factors that shape execution probabilities. The findings show that minority death row inmates convicted of killing whites face higher execution probabilities than other capital offenders. Theoretically relevant contextual factors with explanatory power include minority presence in nonlinear form, political ideology, and votes for Republican presidential candidates. Inasmuch as there is little or no systematic research on the individual and contextual factors that influence execution probabilities, these findings fill important gaps in the literature.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".