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Record W4236007889 · doi:10.24908/iqurcp.8583

Public Perception of Sexual Offence: A Comparison

2018· article· en· W4236007889 on OpenAlexvenueno aff
Cody Sebben

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCommitPunishment (psychology)PsychologyPerceptionSocial psychologySex offenderCriminologySex offenseHuman factors and ergonomicsSexual abusePoison controlMedical emergencyMedicine

Abstract

fetched live from OpenAlex

Studies show that, given the opportunity, most people would punish perpetrators of sexual assault more severely than those who commit other personal injury offences (Roberts, 1990). This study will attempt to explain why most people would prescribe harsher punishment to sexual offenders. Participants will take part in answering one of two questionnaires for the purpose of data collection, each with control variables. It is hypothesized that specific factors play a role in the belief that sexual offenders are a greater threat to individual and public safety than other offenders. These hypothesized factors include: risk to individual and public safety, lack of understanding with regard to sexual offences, belief that the offender has a greater likelihood to reoffend than non sexual offenders, and perception that treatment for sexual offending is not effective. Results from the study are anticipated to help explain why sexual offences are often thought to be more deserving of punishment than most other offences. It is anticipated that results will assist in providing a more complete understanding of sexual offences, both in public perception and in treatment.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.005
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.338
GPT teacher head0.469
Teacher spread0.131 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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