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Record W2765427200 · doi:10.1177/1073191117737377

MTC Sadism Scale: Toward a Dimensional Assessment of Severe Sexual Sadism With Behavioral Markers

2017· article· en· W2765427200 on OpenAlexaff
Nicholas Longpré, Jean‐Pierre Guay, Raymond A. Knight

Bibliographic record

VenueAssessment · 2017
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPsychologyClinical psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

Sadism was initially described as the experience of sexual pleasure produced by acts of cruelty and bodily punishment. Sadism was conceptualized as if sadists were fundamentally different from nonsadists. Recent studies have suggested that sadism is distributed as a dimension rather than as a category. The aim of the current study was to assess the psychometric properties the MTC Sexual Sadism Scale. Our analyses were conducted on a sample of 486 sexual offenders assessed at a correctional institution in Massachusetts. In summary, the results indicate that the MTC Sexual Sadism Scale possesses good psychometric properties for the dimensional assessment of severe sexual sadism with behavioral markers. Moreover, the scale captures a wide range of intensity of sadism among sexual offenders. These results are consistent with prior research and support the current consensus to move toward a dimensional interpretation of sadism. Implications both for clinical assessment and for research on the development of sadism 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.402
Teacher spread0.360 · 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 source (direct Gemma or distilled Codex), 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

Citations41
Published2017
Admission routes1
Has abstractyes

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