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Record W2953633261 · doi:10.29173/jjs41s

Male Friendship As Masculine Individuation in Romeo and Juliet

2016· article· en· W2953633261 on OpenAlexvenueno aff
Halide Aral

Bibliographic record

VenueJournal of Jungian Scholarly Studies · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicShakespeare, Adaptation, and Literary Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsMasculinityIndividuationAndrogynyRelation (database)Tragedy (event)ChristianityPsychoanalysisTricksterIdentity (music)Resistance (ecology)Unconscious mindNothingSociologyAestheticsPhilosophyLiteraturePsychologyArtEpistemologyTheology

Abstract

fetched live from OpenAlex

The purpose of this essay is to analyse from a Jungian perspective how heroic masculinity and Christianity, due to their negative attitude toward the feminine, problematize masculine individuation and cause tragedy in Romeo and Juliet. Although all male characters in Verona fall short of the mature masculinity that could come with developing a relation to the feminine, I focus on Romeo and Mercutio whose problematic development clarifies man’s difficulty with integrating the feminine without forgoing the masculine structure. Romeo, the puer, who represents the spirit, suffers from a positive mother complex. Mercutio, the trickster, the dark side of the puer, represents the body which is considered evil by Christianity, and has a disturbed relation to the feminine. Hence he compensates for, completes, and gives body to Romeo who is otherwise nothing but the spirit. Being the evil component, Mercutio is essential to the individuation process, and with his simultaneous resistance to and what seems to be an unconscious identification with the feminine, Mercutio serves as a medium through which Shakespeare presents what we may now call, following Eugene Monick’s model, bisexual androgyny as an alternative to heroic masculinity. But this potential as embodied in Mercutio is wasted tragically by the heroic masculinity in Verona.

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)0.001
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 score0.594
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
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 head0.062
GPT teacher head0.285
Teacher spread0.224 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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