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Record W1814163830 · doi:10.3138/cjhs.242-a4

The sexual side of castration narratives: Fiction written by and for eunuchs and eunuch “wannabes”

2015· article· en· W1814163830 on OpenAlexaffvenue
Ariel B. Handy, Richard J. Wassersug, James T. J. Ketter, Thomas W. Johnson

Bibliographic record

VenueThe Canadian Journal of Human Sexuality · 2015
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOrgasmNarrativePsychologyFantasySex organSexual abuseSexual desireHuman sexualityPsychoanalysisMedicineLiteratureGender studiesSexual dysfunctionArtSociologySuicide preventionPoison control

Abstract

fetched live from OpenAlex

The Eunuch Archive is an online community of individuals with exceptional interest in castration and penectomy. Here we examine themes related to genital ablation in a sample of fictional stories posted by members of the Eunuch Archive. Similarities between the contents of these stories and members' demographic information were found, suggesting that these stories may reflect some of the members' life experiences or personal fears. Common themes in both stories and personal histories of voluntarily castrated men were homosexuality, childhood abuse, and threats of castration. We found that 83% of stories were explicitly sexual, which was defined as containing physical or mental sexual arousal; sexual acts such as masturbation, oral sex, or penetrative sex; or attainment of orgasm. Fifty-one percent of stories described forced castrations, 34% involved minors, and 24% described orgasms related to genital ablation. Writing these stories may be therapeutic for the authors, as some members have claimed that writing them has allowed them to work through their extreme castration ideations without acting on them. Clinicians should be aware that there are men who express profound interest in genital ablation and their interests and/or concerns should be taken seriously.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.280
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.094
GPT teacher head0.371
Teacher spread0.277 · 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

Citations7
Published2015
Admission routes2
Has abstractyes

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