Dead Wrong: Capital Punishment, Wrongful Convictions, and Serious Mental Illness
Bibliographic record
Abstract
Serious mental illness (SMI), wrongful convictions, and capital punishment is explored, as having a SMI may heighten an individual’s risk of being wrongfully convicted and consequently dealt a capital sentence. In Atkins v. Virginia, the Supreme Court banned the use of capital punishment for individuals with intellectual disabilities, ruling it unconstitutional, due to the diminished moral and intellectual capacity held by these individuals. Based on these Supreme Court findings, an argument is made that SMI is a compelling mitigating factor that ought to disqualify the pursuit of capital punishment. Due to the cognitive and volitional impairments associated with SMI, people with SMI are especially vulnerable to being wrongfully convicted of a crime and further wrongfully sentenced to death. Data to build this argument include that those with SMI are more likely to: 1) falsely confess; 2) struggle with assisting in their defense; 3) be perceived as an unreliable witness; 4) appear as though they lack remorse; and 5) face prejudices from judges and jurors; which all contribute to wrongful convictions. An explanation of these vulnerabilities are discussed in detail by examining 26 case vignettes (derived from the National Registry of Exonerations and other sources) where such individuals were wrongfully convicted due to SMI. Data from the National Registry of Exonerations is further analyzed, leading to discussion of the disproportionate co-occurrence of wrongful convictions that are stimulated by SMI. This paper concludes with an analysis of reforms and a discussion of how to enact safeguards to protect individuals with SMI.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".