How Should Complainer Anonymity for Sexual Offences be Introduced in Scotland? Learning the International Lessons of #Letherspeak
Bibliographic record
Abstract
It is often claimed that complainers in sexual offence cases have an “automatic right to lifelong anonymity in UK law.” While this is true in England, Wales and Northern Ireland – Scots law currently imposes no automatic restrictions on the identification of people who say they have been victims of rape and other sexual offences. Underpinned by a comparative analysis of twenty common law jurisdictions – including Ireland, India, Bangladesh, Singapore, Hong Kong, Canada, New Zealand and Australia – this article considers how complainer anonymity could and should be introduced in Scotland. This article is in three main parts. The first considers the reasons for granting anonymity to complainers in sexual cases. The second explores how complainer anonymity is realised in the laws of the twenty comparator jurisdictions considered in this study, and the key similarities and differences in their approaches to imposing reporting restrictions. Drawing on the experience of the # LetHerSpeak campaign in Australia, the third section considers critical design choices the Scottish Government faces in legislating for complainer anonymity, including decisions on when a right to anonymity accrues, what offences it applies to, and in what circumstances – and by whom – it can be waived or set aside.
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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 | 0.026 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".