What Keith Bush’s Exoneration Teaches Us About Wrongful Convictions
Bibliographic record
Abstract
Bush spent 33 years in prison for the attempted rape and murder of a 14-year-old girl on Long Island. 1 He was exonerated at an emotional court hearing in Suffolk County. 2 For those interested in working in the field of wrongful convictions, or those who just have a general interest in these types of cases, Mr. Bush's case presents a microcosm of the criminal justice system's flaws and an example of what it takes to overturn a decades-old conviction.What follows are some of the key principles that are illuminated in the case. I. PEOPLE -ESPECIALLY YOUNG PEOPLE -DO FALSELY CONFESS TO CRIMES THEY DIDN'T COMMITDNA evidence has shown that people falsely confess to crimes they did not commit. 3 Nearly a quarter of all DNA exonerations involve a person who confessed to the crime. 4Some of the most high-profile exonerations -those of the Central Park Five 5 ; Marty Tankleff; 6 This essay is based on the comments from the symposium and a blog post which is available at: https://courtroomstrategy.com/2019/05/what-keith-bushs-exoneration-teaches-us-about-wrongful-convictions/.* Oscar Michelen is a partner at Cuomo LLC, with extensive litigation experience in
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".