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Record W4301400346 · doi:10.3366/elr.2022.0783

How Should Complainer Anonymity for Sexual Offences be Introduced in Scotland? Learning the International Lessons of #Letherspeak

2022· article· en· W4301400346 on OpenAlexaboutno aff
Andrew Tickell

Bibliographic record

VenueEdinburgh Law Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsAnonymityGovernment (linguistics)LawPolitical scienceCommon lawScotsNorwegianSociology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.118
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.047
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.012
Scholarly communication0.0090.007
Open science0.0020.004
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.100
GPT teacher head0.368
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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