Bibliographic record
Abstract
The disclosure of evidence, primarily from the prosecutor to the defense (i.e., discovery) is key to a fair and just legal system. Restrictive discovery policies have been criticized for contributing to innocent defendants pleading guilty (Alkon, 2014) and to uninformed plea decisions (Friedman, 1971). Open-file policies, in which prosecutors broadly share evidence with the defense, are a leading reform to address these issues. This study investigated the impact of guilt and access to discovery information (with or without exculpatory evidence) on plea decisions. We hypothesized that, in comparison to their counterparts, participants who had access to all of the evidence (i.e., those in open-file condition) and participants who were innocent would rate the evidence against them as significantly weaker, their probabilities of conviction at trial as significantly lower, and would be less likely to take the plea deal. We also hypothesized that ratings of evidence strength and probability of conviction would mediate expected relations between the plea decision and conditions. One-hundred participant-defendants were randomly assigned to open- vs. closed-file and guilt vs. innocence conditions, and asked to review case materials that either contained full or partial discovery. They were then asked to rate the strength of the evidence against them, their probability of conviction, and to accept or reject a plea offer in a hypothetical case. Defendant guilt and access to discovery information impacted perceived evidence strength, which subsequently impacted plea decision-making. Our findings indicate that access to discovery information indirectly impacted defendants’ plea decisions.
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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 | 0.010 | 0.061 |
| 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 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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; 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".