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Record W3188506034

Sauce for the Goose is Not Sauce for the Gander – Male Victims of Rape

2021· article· en· W3188506034 on OpenAlexaboutno aff
Alexander Ng

Bibliographic record

VenueSSRN Electronic Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsnot available
Fundersnot available
KeywordsStatuteCriminologyStatutory lawLawInterpretation (philosophy)Punitive damagesPolitical scienceMasculinityStatutory interpretationCriminal lawPsychologySociologyGender studies
DOInot available

Abstract

fetched live from OpenAlex

This essay focuses on the issue of male rape. It is recognised that some parts of the essay contain sensitive information in which certain individuals may find disturbing or discomforting. Male rape is a significant social and legal issue. Male victims of sexual offences were traditionally not given equal protection under statute as compared to their female counterparts. Although significant improvements have been made in the Sexual Offences Act 2003, there is still room for improvement. Inspiration can be sought from other common law jurisdictions, namely Australia and Canada, to see how current rape law can be improved. Judicial interpretation, however, also has to be improved to give effect to the strides made in statutory law. There remain significant misunderstandings in male biology, particularly the processes of erection and ejaculation during sexual assault, which can be traced to traditional ideals of masculinity. This can be resolved by adopting modern medical understanding of male physiology. If such problems in the current law are not recognised and rectified, it is submitted that serious ramifications may arise in relation to further stigmatisation of male victims and increased acquittals of dangerous sex offenders. Only by putting male and female victims of sexual offences on equal footing can true gender equality be realised.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.037
GPT teacher head0.342
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 teacher head, not a consensus.

Study designNot applicable
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

Citations0
Published2021
Admission routes1
Has abstractyes

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