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Record W4366782189 · doi:10.29173/jjs222s

Ensnare

2023· article· en· W4366782189 on OpenAlexvenueno aff
Christina Forbes-Thomas

Bibliographic record

VenueJournal of Jungian Scholarly Studies · 2023
Typearticle
Languageen
FieldPsychology
TopicParanormal Experiences and Beliefs
Canadian institutionsnot available
Fundersnot available
KeywordsSoulPsychicMythologyAestheticsDehumanizationTotemSociologyPsychoanalysisLiteraturePsychologyEpistemologyPhilosophyArtAnthropology

Abstract

fetched live from OpenAlex

The present research blends archetypal and feminist perspectives, along with current research into the hemispheres of the brain, to investigate the psychological implications of the pursuit and attempted murder of Ambrosia, a nymph and nursemaid to Dionysus, by King Lycurgus of Thrace in Ancient Greece. A depth psychological story of psychic activism, feminine liberation, and transformation, “Ensnare” builds around a single image from a piece of Greco-Roman artwork—the attack and attempted murder of Ambrosia by Lycurgus, whose deeds evoke the destructive forces of literalism, monotheistic temperaments, intolerance to diversity, exclusively rationalistic attitudes, and patriarchal systems that deaden the imagination and imperil the unfolding of soul. Theoretically, this project of creative womanhood relies upon Hillman’s (1975) four modes of re-visioning psychology: personifying, or imagining things; pathologizing, or falling apart; psychologizing, or seeing through; and dehumanizing, or soul-making. Following Hillman, “Ensnare” invites the reader to find and make soul through a non-literal attitude of fantasying that creatively engages the imagination and images of female empowerment from the myth. The aim of this imaginal engagement with the mythological figures of Ambrosia, Lycurgus, Gaia, and Athene is to discover and partner with the archetypal presences who supported Ambrosia’s liberation as we work to bring the meaning of her initiatory experience to our own ideas and ways of being.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.173
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.100
GPT teacher head0.411
Teacher spread0.311 · 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.

Study designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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