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Touch, Intimacy, and Sexuality in Partnership and Domination Environments

2019· book-chapter· en· W2969514923 on OpenAlexaboutno aff
Riane Eisler

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsHuman sexualityGeneral partnershipSociologyPsychologyGender studiesSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Abstract How people are touched, especially as children and in sexual and other intimate relations, affects and is in turn affected by cultural factors. This chapter explores how patterns of touch, intimacy, and sexuality differ at opposite ends of the domination-partnership continuum and why understanding this is important for moving forward. Studies show that we read other’s intentions and emotions by how we are touched and that the confluence of caring with coercion and pain is one of the most effective mechanisms for socializing people to suppress empathy and submit to domination as adults—whether through the painful binding of girls’ feet once traditional in China, or so-called Christian parenting guides that today admonish parents not to “overindulge” children and instead follow “God’s way” by forcing eight-month-old babies to sit with their hands on their trays or laps through threats and violence. Sexuality, too, is distorted in domination systems through the erotization of domination and violence, for example, by inculcating the belief that males are entitled to sex; through the mass shootings of women in the United States and Canada by men who call themselves incel (involuntarily celibate); and by the enslavement of women by Muslim fundamentalist groups like ISIS. The chapter contrasts these unhealthy interactions with healthy ones supported by partnership-oriented cultures.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.006
Scholarly communication0.0040.002
Open science0.0000.001
Research integrity0.0000.002
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 head0.338
Teacher spread0.288 · 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 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
Published2019
Admission routes1
Has abstractyes

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