What Keith Bush’s Exoneration Teaches Us About Wrongful Convictions
Bibliographic record
Abstract
Keith 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.4 Some 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 Federal and State courts particularly in commercial litigation, intellectual property, municipal liability, white collar criminal defense and representing management in labor and employment litigation.He has exonerated six men in five cases over the past fourteen years. 1 Arielle Dollinger, No One Would Listen: Cleared of Murder After 33 Years, N.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".