Wrongful Convictions: The American Experience
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The increasing number of high-profile cases of wrongful conviction, often brought to light by DNA exonerations, and the publicity associated with those errors have increased the salience of this issue on the public policy agendas of a number of U.S. states, as well as in Canada. Scholarly research on this subject has also increased over the past two decades. This article discusses the extent to which these errors may occur; the major factors contributing to false convictions; recent and current developments regarding legislation in the United States; innocence projects and innocence commissions in the United States, Britain, and Canada; and the significance of wrongful conviction as a factor in the current challenges to the death penalty in the United States. It is important that we develop a better understanding of wrongful conviction and its causes so that we can both better protect the rights of the innocent and better protect citizens from being victimized by offenders who remain free while the wrongly convicted are sent to prison.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it