Expert Scientific Evidence in the Investigation and Prosecution of Child Sexual Abuse in Adversarial Jurisdictions
Bibliographic record
Abstract
This chapter explores some problematic areas for expert scientific evidence in the investigation and prosecution of cases of child sexual abuse (CSA), an area of increased social importance. In particular, it focuses on the difficulties of gathering sufficient evidence to justify a prosecution and the challenges concerning trustworthiness presented by emerging science. Each of these areas exemplifies the close dependency between the two disciplines while also revealing the tensions that shape their interactions more generally (Jasinoff, 1995; Redmayne, 2001). The chapter draws on examples from the common-law countries, that is, English-speaking jurisdictions with an adversarial background: Australia, Canada, England and Wales, Ireland, New Zealand, North America and Scotland. The disciplines of law and science have a unique and enduring relationship. They perform a distinctive role within the adversarial criminal justice system. The rules for admissibility of expert evidence in adversarial jurisdictions share a common heritage Introduction 351 Science, Certainty and Miscarriages of Justice 352 Role of Expert Evidence in Child Sexual Abuse Prosecutions 354 Barriers to Prosecution 355 Overcoming Barriers to Prosecution: Obtaining Evidence 356 Potential of Emerging Science 361 Conclusion 363 References 363 and a broad set of principles, values and anticipated outcomes. Although the law in these jurisdictions is subject to regional variation, the science has universal application. As such, the practices in all countries following an adversarial approach have relevance for one another.
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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 | 0.186 | 0.258 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.012 | 0.051 |
| Scholarly communication | 0.026 | 0.025 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.021 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 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".