Bibliographic record
Abstract
Modern research has been diligent and successful in discovering what causes a wrongful conviction and long-term consequences on the wrongfully convicted person and their family. However, there is one area that remains relatively untouched by research efforts. It is the period between the conviction and the release, the period of incarceration itself. The purpose of this paper is to outline the experiences of wrongfully convicted persons in prison. While each incarceration term is an individualized experience, there are many commonalities within these experiences. This paper will consider the incarceration experience via two lenses: Part I will look at inmate and prison violence, and Part II will explore mental health and segregation. The paper will focus largely on the Canadian perspective, with limited insights from other jurisdictions. Each section will also evaluate: (1) the general prison experience for all incarcerated persons, and (2) the distinct prison experiences of the wrongfully convicted as a result of maintaining their innocence. Because little research exists on the distinct experiences of wrongfully convicted persons in prison, this paper looks to interviews and other sources where wrongfully convicted persons discussed their prison experiences. These sources are few and far between, with many wrongfully convicted persons echoing the words of Thomas Sophonow (wrongfully convicted of the murder of a 16-year-old donut shop employee), “whatever happened in jail [is] nobody’s business.”
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".