Take the plea: the factors that influence innocent individuals to accept plea bargains
Bibliographic record
Abstract
Recently, plea bargaining has emerged as a factor that contributes to wrongful convictions. When a Crown offers a reduced sentence or lesser charge to a defendant in exchange for a guilty plea, there is the potential for innocent defendants to plead guilty. However, little is known about the factors that are influencing innocent defendants to accept plea bargains. The current study aimed to investigate the role of false evidence, risk, and modality on an innocent participant’s likelihood of accepting or rejecting a plea bargain. In a laboratory, innocent participants (N = 174) were accused of collaborating with another participant (confederate) on a problem solving task, and offered a plea bargain. Results showed that when participants were told there was an 80% chance of sanctions if they rejected the plea, they were more likely to admit guilt, and accept the plea. Additionally, participants who were high in compliance, high in fantasy proneness, or were younger, were more likely to accept the plea bargain. Implications of these findings for innocent defendants are discussed.
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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 | 0.004 | 0.052 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".