Bibliographic record
Abstract
For three decades, the innocence movement has focused on proving “factual innocence” with DNA evidence. Substance. But as Professor Medwed details, far more people are wrongly convicted than those who can rely on exculpatory DNA evidence. DNA has been crucial to exposing the many causes of wrongful convictions: faulty forensic evidence, police and prosecutor misconduct, mistaken eyewitnesses, unreliable informants, false confessions, and racism. DNA opens the doors to recognizing these other causes of wrongful convictions. But what next? Barred walks us through the procedural bars and barriers at each step a wrongly convicted person takes toward freedom. As Medwed describes it, “the rule regime is stacked against the innocent, contrary to the popular belief that the postconviction process is full of escape hatches from the prison cell, those imaginary ‘technicalities’ that let people loose…. You can have evidence of innocence – and no one willing to hear it.” Through the pages of Barred, Medwed turns us to procedure for the next stage of innocence work. If it is procedure that creates the bars, then it is those bars we must bend to free innocent people.
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.
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 | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".