Bibliographic record
Abstract
In the past three decades, North America has exonerated over 3,400 innocent people of crimes they did not commit—with nearly 300 of those exonerees being women. Recent years have seen a 700% increase in female incarceration, which could influence future rates of wrongful convictions among women as well. The existing literature on wrongful conviction disproportionately focuses on male exoneree experiences and stories, leaving female exoneree needs and experiences entirely unaccounted for. The following review identifies the relevant literature pertaining to the lived experiences of mothers, exonerees, and incarcerated women to address the gaps in the wrongful conviction literature and inform future research projects. Evidenced by this review is that systematic differences are leading to the wrongful conviction of women, women experience different pains of imprisonment and may be at a disproportionate risk of mental and physical health complications due to their wrongful conviction and incarceration. Future research must focus on the unique lived experiences of female victims of wrongful convictions to understand the mechanisms underlying their convictions, and their unique experiences of wrongful conviction, incarceration, re-entry, and victimization, to adequately inform policy and help in their re-entry and rehabilitation.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".