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Record W4287624441 · doi:10.5281/zenodo.4162514

Lethal Outcome in Elderly Sexual Violence: Escalation or Different Intent?

2020· article· en· W4287624441 on OpenAlexaff
Éric Beauregard, Julien Chopin, Winter Jan Martin

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSexual violenceOutcome (game theory)De-escalationPsychologyClinical psychologyMedicineMedical emergencyCriminologyIntensive care medicineEconomics

Abstract

fetched live from OpenAlex

Purpose: The study examines violent sexual offenses against elderly victims that resulted in either serious injuries or death and explore whether certain components of the crime-commission process explain the different outcomes. The study investigates the question of whether a lethal outcome in elderly sexual assaults is the result of an escalation in violence or a different intent. Methods: Bivariate and logistic regression analyses were conducted on a sample of 199 offenders convicted of a violent sexual offense against an elderly woman that resulted in either severe physical injury (n = 145) or death (n = 54) of the victim. Results: Results showed that violent sexual offenses ending with the death of the victim were more likely to be characterized by the use of a weapon, foreign object insertion, and the taking of items belonging to the victim. Violent sexual offenses ending with serious physical injuries were characterized by the presence of penetration (anally and vaginally) as well as ejaculation in or on the victim. Conclusions: Differences observed between the two groups suggest that offenders who killed the victims had the intent do so compared to those offenders who inflicted several physical injuries. Practical implications of the findings are discussed.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.002

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 head0.099
GPT teacher head0.318
Teacher spread0.219 · 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 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicElder Abuse and NeglectFrench-language works237,207