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Record W2085683702 · doi:10.1558/ijsnr.v4i2.177

Blood, Sweat, and Urine

2014· article· en· W2085683702 on OpenAlexaff
Cimminnee Holt

Bibliographic record

VenueInternational Journal for the Study of New Religions · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicReligious Studies and Spiritual Practices
Canadian institutionsConcordia University
Fundersnot available
KeywordsWitchMAGIC (telescope)TabooLiteratureOccultRhetoricAestheticsFeminismFantasyPhilosophySociologyPsychoanalysisArtPsychologyGender studiesAnthropology

Abstract

fetched live from OpenAlex

Anton Szandor LaVey wrote The Satanic Witch in 1970 as a response to the contemporary discourses of his time: feminism and the occult revival. This essay focuses on LaVey’s treatment of the scent of feminine fluids blood, sweat, and urine—in The Satanic Witch and selected texts in order to demonstrate that LaVey’s emphasis on the importance of bodily secretions is an extension of his carnal-magical worldview; he employs the arcane language and aesthetics of the occult to methods of physiological and psychological manipulation in order to influence others and achieve desired ends. Throughout this article I apply Mary Douglas’ theories in Purity and Danger (2002 [1966]), which address our notions of contagion, dirt, and taboo; feminist rhetoric on 1960s and 1970s feminine hygiene products and their putative cleansing of natural feminine scent; and finally, the use of sexual fluids in esoteric magical practices such as described by Aleister Crowley. This article illustrates that LaVey’s use of feminine fluids for magical efficacy reflects his notion that magic is firmly rooted within one’s own body, and the capacity of one’s own will, while also incorporating and responding to the surrounding discourses of his time.

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 score0.004
Threshold uncertainty score0.013

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 studies0.0030.011
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.316
Teacher spread0.268 · 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".

Quick stats

Citations2
Published2014
Admission routes1
Has abstractyes

Explore more

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