The psychological allure of Alford: Does wanting to appear innocent put innocents at risk?
Bibliographic record
Abstract
OBJECTIVE: The Alford plea allows defendants to maintain innocence while pleading guilty, but this option is largely unknown to the public, and its effects are unknown to researchers and practitioners. Some legal scholars have argued that the Alford plea may attract innocent defendants who may not otherwise accept a plea, whereas others have asserted that it offers a beneficial alternative for those wanting to preserve their reputations and avoid the more severe consequences of a trial conviction. Applying a social psychological lens, we examined how the Alford plea influences innocent and guilty mock defendants' plea decision making. HYPOTHESES: We predicted that whereas guilty mock defendants would be more likely to accept a plea overall, the Alford variant would increase the rate at which innocent mock defendants accept pleas. METHOD: We conducted two studies: In Study 1, 406 Prolific Academic participants read a vignette in which they were either innocent or guilty of involuntary manslaughter; in Study 2, we used an interactive simulation of legal procedures in which 367 innocent Testable Minds participants were accused of larceny. In both studies, participants were offered either a traditional plea requiring them to admit guilt or an Alford plea allowing them to maintain innocence. RESULTS: = 3.61, 95% CI [1.28, 10.20]) if it was an Alford (and allowed them to maintain innocence). Additionally, many participants in both studies cited self-presentation-related reasons for their plea decisions. CONCLUSION: Our findings suggest that the Alford plea may increase the risk of false guilty pleas, a finding that has important implications for criminal defendants and the attorneys who advise them. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".