Bibliographic record
Abstract
Wrongful convictions for imagined crimes that did not happen, including witchcraft and satanic sexual abuse, have been influenced by gender stereotypes. The role of intersecting forms of prejudice is examined through case studies of wrongful child abuse convictions of a gay man in New Zealand and lesbians in the United States. Case studies of the wrongful convictions of Florence Maybrick, Lindy Chamberlain and Kathleen Folbigg are related not only to their immediate cause of faulty forensics but also to perceived departures from gendered concepts of motherhood and wifehood. A similar theme is seen in the disproportionate wrongful convictions and false guilty pleas of women for the deaths of children in their care. Shaken baby syndrome played a role, but gender, racial and class prejudices were also often in play. Women, especially Indigenous women, are particularly vulnerable to making false guilty pleas. The possible role of stereotyped assumptions about male violence in sexual assault wrongful convictions will be examined. Sexual assault law reforms make it more difficult to correct wrongful convictions where consent is claimed as opposed to the minority of cases of stranger sexual assaults where DNA can prevent and remedy wrongful convictions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".