Prosecutorial Discretion and the Death Penalty: An Integral Perspective
Bibliographic record
Abstract
The prosecutor’s choice to pursue the death penalty is one of the most momentous decisions he or she will face. Capital punishment represents the ultimate power of the state over its citizenry, and the decision to take the life of an offender is fraught with moral complexity. This paper reviews some of the extant literature on the US death penalty in general and the particular issue of decision-making for prosecutors. Further, we introduce discussion on how Wilber’s Integral theory might be applied to the topic. We present aspects of Integral theory, including the four quadrant model and what Wilber refers to as the Basic Moral Intuition (BMI), as possible tools that may be used to navigate the ethical difficulties surrounding this decision-making process. We anticipate that delving into aspects of the Integral theory and contemplating on how they relate to concrete issues of criminal prosecution may assist CJ practitioners in how they might find pathways to resolutions of ethical quandaries.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.046 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| 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".