Bibliographic record
Abstract
People with a mental illness (PWMI) are among the most vulnerable populations in the country, yet are far more likely to be incarcerated than people without a mental illness. PWMI are more likely to be wrongfully convicted for several reasons.At the onset of an investigation, PWMI are more likely to become suspects. Symptoms of mental illness breed fear and misunderstanding, arousing suspicion of a PWMI in the first place. Once approached by police, PWMI are more likely to escalate the initial encounter, leading to arrest and further interrogation. Through the lens of the Reid Technique, police misinterpret symptoms of mental illness as signs of guilt. Police continue using the Reid Technique to extract a confession. Mid- interrogation, PWMI are less likely to invoke Miranda rights. Without counsel, PWMI are more susceptible to minimization and maximation techniques, leading to higher rates of false confessions and ultimately, false convictions. These issues are significantly exacerbated for PWMI of color, who experience additional racial bias. From the beginning of an investigation to the end, the justice system seems perversely calculated to target innocent PWMI, rather than protect them. The case of James Blackmon demonstrates how an innocent PWMI can be railroaded into a false confession and wrongful conviction. This paper details Blackmon’s case, analyzes how each step of an investigation endangers PWMI, and examines possible solutions to protect innocent PWMI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".