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Record W3097955810 · doi:10.1002/9781118970843.ch285

Sadism and Masochism

2020· other· en· W3097955810 on OpenAlexaff
Natasha Knack, Lisa Murphy, J. Paul Fedoroff

Bibliographic record

VenueThe Wiley Encyclopedia of Personality and Individual Differences · 2020
Typeother
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsSadomasochismPleasureCrueltyPsychologyHuman sexualityInterpretation (philosophy)PsychoanalysisPain and pleasureParaphiliaSexual intercourseSocial psychologySexual behaviorPsychotherapistSociologyCriminologyPhilosophyGender studies

Abstract

fetched live from OpenAlex

The terms sadism and masochism were coined by German psychiatrist Richard von Krafft-Ebing in his book Psychopathia Sexualis. He described sadism as deriving sexual pleasure from inflicting pain on another person and masochism as obtaining sexual pleasure from receiving pain and cruelty inflicted by another person. British psychologist Havelock Ellis promoted the idea that sadism and masochism are actually closely related concepts as they describe complementary forms of behavior. This new interpretation formed the basis of the modern conception of sadomasochism (SM). The Kama Sutra, which is one of the earliest descriptions of human sexuality, not only compares sexual intercourse to a quarrel but also discusses a range of embraces including scratching, biting, and striking, and gives advice about where and how to administer these embraces, as well as the different sounds that will result. Research has found that SM practitioners are typically well-adjusted and comfortable with their sexual preferences.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.008
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.049
GPT teacher head0.300
Teacher spread0.251 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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