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Hebrew Bible Goddesses and Modern Feminist Scholarship

2012· article· en· W2145487056 on OpenAlexaff
Peggy L. Day

Bibliographic record

VenueReligion Compass · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicBiblical Studies and Interpretation
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsScholarshipHebrew BibleHebrewOld TestamentHeavenNew TestamentHuman sexualityLiteratureWorshipBiblical studiesHistorySociologyPhilosophyTheologyGender studiesArtLaw

Abstract

fetched live from OpenAlex

Abstract This essay first traces the association of ancient goddesses with so‐called fertility cults and sacred (or cultic) prostitution to J. J. Bachofen’s once influential model of human social development, and places prefeminist Hebrew Bible/Old Testament scholarship on this trajectory. It then discusses two streams of feminist theological response to the model, a Great Goddess stream that inverted the values that traditional scholarship associated with ancient goddesses and their worship, and a revisionist stream that strove to reimage the God of the Hebrew Bible to include aspects of the feminine divine. Both streams are assessed critically from the perspective of secular feminist scholarship. Feminist scholars’ challenges to the stereotyping of Canaanite goddesses as primarily associated with sexuality and reproduction are then presented, followed by feminists’ critiques of both the relevance and veracity of the ancient sources alleged to be evidence for the practice of sacred (or cultic) prostitution. Finally, the essay turns to the texts of the Hebrew Bible/Old Testament per se and focuses on the three Hebrew Bible goddesses most discussed in the secondary literature, i.e., Asherah, Astarte/Ashtart and the Queen of Heaven.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0060.030
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0110.000

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 head0.054
GPT teacher head0.275
Teacher spread0.221 · 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 designTheoretical or conceptual
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

Citations2
Published2012
Admission routes1
Has abstractyes

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