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Record W2617350684 · doi:10.1177/0734016817704700

Continuous Child Sexual Abuse

2017· article· en· W2617350684 on OpenAlexaff
Dayna M. Woiwod, Deborah A. Connolly

Bibliographic record

VenueCriminal Justice Review · 2017
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsStatuteLegislationSexual abuseWork (physics)PsychologyFace (sociological concept)LawComputer securityPolitical scienceHuman factors and ergonomicsPoison controlSociologyEngineeringComputer scienceMedicineMedical emergency

Abstract

fetched live from OpenAlex

Due to calls for reform of legislation that accounts for the difficulties complainants of repeated child sexual abuse (CSA) face when asked to particularize individual acts, jurisdictions in the United States and Australia have adopted continuous CSA statutes. Continuous CSA statutes allow for reduced particularity of individual instances when abuse is repeated. In this article, we discuss particularization requirements and how they are adapted in current jurisdictions in the United States and Australia with continuous CSA statutes. We then discuss the relevant research on children’s memory for repeated events and frequency to discuss how current and future research can inform the criteria for the charge. Our goal in this article is to inspire thoughtful discussion of continuous CSA legislation, and how current and future psychological research can advance the criteria for the charge. As more jurisdictions consider adopting these statutes, it would be helpful for psychologists and legal professionals to work toward developing a consensus on the criteria for the charge that balances both the victim’s capabilities to particularize repeated CSA and various rights of the accused.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.074
GPT teacher head0.379
Teacher spread0.305 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations25
Published2017
Admission routes1
Has abstractyes

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