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Record W4393235436 · doi:10.3390/sexes5020004

Comparing Attitudes toward Sexual Consent between Japan and Canada

2024· article· en· W4393235436 on OpenAlexaffabout
Tomoya Mukai, Chantal Pioch, Masahiro Sadamura, Karin Tozuka, Yui Fukushima, Ikuo Aizawa

Bibliographic record

VenueSexes · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsPsychologyPolitical scienceGender studiesSociology

Abstract

fetched live from OpenAlex

Japanese and Canadian laws regarding sexual assault vary in the degree to which they incorporate the concept of sexual consent, with Japanese law being less consent-oriented than Canadian law. Although the Japanese law has incorporated the concept of sexual consent in the 2023 amendment, the public understanding of the concept is still limited. Reflecting such difference, it could be expected that the general public in both countries also differ in their perceptions and attitudes regarding punishment of sexual crime and sexual consent. The present study aimed to test these expectations and further examine the mediational mechanism that explains the national difference between Japan and Canada. The data from 1125 Japanese and 1125 Canadian respondents showed that Japanese respondents were less likely to perceive the imposition of punishment on an alleged perpetrator described in scenarios as appropriate. In contrast, the difference in the perceived victim’s consent was significant only in three out of seven scenarios. The relation between nations (Japan vs. Canada) and perceived appropriateness of punishment was mediated by the perceived victim’s consent.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score0.249

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.107
GPT teacher head0.361
Teacher spread0.254 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations7
Published2024
Admission routes2
Has abstractyes

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