Bibliographic record
Abstract
Historically, the literature on wrongful convictions has focused on a relatively small number of high-profile exonerations of convictions for serious crimes. However, these cases represent only a small percentage of the total number of wrongful convictions. An emerging area of scholarship is expanding our understanding of their prevalence, by looking at the role that systemic and structural pressures have had on the choice of factually innocent defendants to enter false guilty pleas (FGPs). Plea bargaining – particularly presenting defendants in low-level criminal cases with “offers that they cannot refuse” – needs to be understood as having the potential to increase the number of wrongful convictions. Within this context, this article argues that the increased use of pre-trial detention (PTD) represents a very powerful source of FGPs. Part I discusses the potentially large number of cases affected by FGPs in Canada. Part II explores how the mechanisms of PTD likely influence defendants’ decisions to give a FGP. Part III discusses how the prevalence of FGPs represents a trade-off of the value of a defendant’s innocence in favour of other institutional or societal objectives rooted in the generalized culture of risk aversion and risk management in the Canadian criminal justice system. This emerging scholarship represents a particularly important area of inquiry because, by their very nature, FGPs represent one of the least correctable and answerable parts of the criminal justice system.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".