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Record W2576764381

Deviant Men, Prostitution, and the Internet: A Qualitative analysis of Men who killed Prostitutes whom they met online

2012· article· en· W2576764381 on OpenAlexaboutno aff
Kelly Beckham, Ariane Prohaska

Bibliographic record

VenueInternational Journal of Criminal Justice Sciences · 2012
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsPornographyPsychologyThe InternetChild pornographyAnonymityCriminologyHuman sexualitySadistic personality disorderCyberspacePremarital sexSadomasochismSocial psychologyPersonalityComputer securitySociologySexual behaviorGender studiesPersonality disorders
DOInot available

Abstract

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IntroductionThe Internet offers endless opportunities to whet the appetite of a sexually deviant person. Components of the Internet offer easy, limitless access to the sex industry including cyber sex chatrooms, pornography and websites dedicated solely to escort services and prostitution, e.g., Backpage.com. Because each interest can be pursued with perceived anonymity, the likelihood of deviant sexual experimentation increases (Bell & Lyall, 2000). The types of people that are drawn to sexually deviant Internet content may enjoy sadomasochism. Because sadomasochistic fantasies and behaviors have become more common, it is possible that the number of sexually-driven crimes has increased. Studies of criminal sadists have found serious psychopathic tendencies in addition to sadistic sexual preferences. Substance abuse and personality disorders are also common in these individuals (Forensic Panel Letter, 2001). Studies of serial sexual murderers have shown deviant sexual interests and deviant sexual fantasies (Forensic Panel Letter, 2001).The purpose of this study is to analyze men who have preyed on prostitutes and determine if similarities exist between the offenders who used the Internet to find sexual partners with sexual killers who did not utilize the Internet. Our research will answer multiple questions. First: Is the Internet enabling dangerous sexual behaviors by acting as the medium through which sexually-deviant individuals are able to connect with vulnerable women, i.e., prostitutes? Second: Is there a correlation between men who are obsessed with violent or obscene pornography and those who browse the Internet to contact prostitutes to act out desires? Third: Has the Internet created a new type of offender? We will use life course theory to examine newspaper articles that describe the offenders, their cases, and their life histories in order to assess their sexual pasts and compare them to sexual killers who have not used the Internet. First, we review the literature on prostitution and violence, men who buy sex, and paraphillias and their causes.Literature ReviewProstitution and ViolenceViolence is commonly associated with prostitution. Homicide, then, is unsurprisingly the leading cause of death of prostitutes (Brewer et al., 2006). Between the years 1967 and 1999 prostitutes who worked in Colorado were found to have the highest homicide victimization rate of any other set of women ever studied, with nearly all of the homicides occurring on the job (Potterat et. al., 2004). Clients committed about 65% of prostitute homicides in Canada and the United Kingdom (Kinnell, 2001). Although little research has been conducted on violence against prostitutes, one study by Brewer et al. (2006) discovered that between the late 1980s and early 1990s, large increases in prostitute homicides occurred, with lone perpetrators accounting for the majority of these murders.Violence against sex workers is executed by a small proportion of exceedingly violent men (Lowman & Atchison, 2006). Men target prostitutes because they perceive them as vulnerable and available (Egger, 2002). Due to the fact that prostitution is illegal, the men have a perceived anonymity; believing law enforcement will not notice when the victim is murdered. However, it is still unknown if the slaying of prostitutes occurs because of the profession itself, i.e., hatred of prostitutes (women), or if it is a crime grounded solely on availability-meeting-opportunity, or the combination of a convenient time and location that helps to avoid detection and thus increase offending (Salfati et al., 2008). Many men select prostitutes due to the fact they will not be reported as missing (Quinet, 2011).Examining prostitute homicides committed by clients reveals unclear motives (Brewer et al., 2006). However, various motives may include arguments over the sex/money exchange, victim's attempted robbery of the client, verbal insults, demands or requests by the victim, clientele misogyny, clientele hatred of prostitutes, client's sadism, client's psychopathology, a combination of these factors, or no precipitating factor whatsoever (Brewer et al. …

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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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
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 head0.087
GPT teacher head0.458
Teacher spread0.371 · 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 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

Citations12
Published2012
Admission routes1
Has abstractyes

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