Some unusual cases of multiple-victim, multiple-offender child sexual abuse (1980-2020).
Bibliographic record
Abstract
AIM: To assess the pattern of some unusual cases of child abuse, including their trial and subsequent appeal outcomes, over the last 40 years. METHOD: Cases of multiple-victim, multiple-offender child abuse, occurring in a pre-school or similar setting, without physical evidence of abuse, from developed, English-speaking countries were collected. RESULTS: Thirty cases fulfilled the study criteria: 26 from the US and one each from Australia, New Zealand, Canada and the UK. The first was in 1983 and the most recent in 1994. Of 81 people accused, 43 (53%) were female. One or more defendants were convicted in 19 of the 30 cases (63%). The verdict was subsequently overturned in 13 of 19 (69%) convictions, up to 30 years later. Three additional cases occurred in Europe between 1992 and 2006conclusion: These cases, relying upon children's testimony and evidential interviewing techniques overseen by law enforcement officers, occurred in a cluster from the early 1980s until the mid-1990s, with almost none since. This highly unusual pattern, combined with two thirds of convictions being overturned, supports doubts regarding whether abuse occurred in these children.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".