Predicting expert social science testimony in criminal prosecutions of historic child sexual abuse
Bibliographic record
Abstract
Purpose. Recently courts in several Common Law jurisdictions have been faced with the daunting task of adjudicating criminal complaints of child sexual assault that are alleged to have occurred in the distant past (historic child sexual abuse; HCSA). In the present data set, alleged offences ended between 2 and 48 years before the trial. These cases, which involve claims of repressed memory and continuous memory for the offence, raise many issues that hitherto had only rarely been faced by criminal courts and that are within the realm of issues studied by social scientists. In this paper we explore variables that predict the presence of a social science expert, called by the prosecution or the defence or an expert called by both sides. Methods. A total of 2,064 actual criminal cases involving HCSA were coded on a variety of variables that were then used to predict the presence of an expert at trial and to predict the presence of an expert to evaluate the perpetrator for sentencing. Results. Six variables predicted the presence of an expert at trial: offence description, frequency of abuse, complainant/accused relationship, complainant age, presence of repression, and complainant gender. Seven variables predicted the presence of an expert at sentencing: offence description, frequency of abuse, length of delay to trial, presence of threat, trial date, plea, and age difference between complainant and accused. Conclusions. We use these archival data to generate hypotheses concerning the observed predictors of the use of expert testimony by courts in HCSA cases. The objective is to encourage more controlled studies of the particular case characteristics about which courts seek guidance from social scientists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".