Understanding the Significance & Complexity of the Brady Rule
Bibliographic record
Abstract
My independent study final product is a means of demonstrating the importance and complexity of evidentiary disclosure in the American criminal justice system. To accomplish this, I designed an experiment to evaluate the impact of Brady violations on the outcome of a criminal trial or plea negotiation. The experiment involved presenting two different versions of a fictional criminal case to forty-four volunteer participants, who were randomly organized into two even groups: Group A and Group B. Version A of the case included the totality of the evidence collected during the discovery phase of the case while Version B of the case omitted a single piece of “exculpatory” evidence to produce the effect of a Brady violation. After reading the given facts, participants in both groups A and B were asked to answer questions regarding the defendant’s culpability and the wisest course of legal action. Participants were given a specified period between three weeks and three days to review the facts and submit their answers to the experimental questions. The results of this experiment and my supporting research on federal and state disclosure regimes show that the “materiality” clause in the Brady Rule subjects prosecutors to highly subjective and vague criteria that 1) can easily be exploited by prosecutors with malicious intents and 2) increase the risk of unintended Brady violations by good-meaning prosecutors.
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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 | 0.018 | 0.104 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".