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Record W4366782742 · doi:10.29173/jjs219s

The Castaway Archetype in Two Tales of an Island Year

2023· article· en· W4366782742 on OpenAlexvenueno aff
Matthew A. Fike

Bibliographic record

VenueJournal of Jungian Scholarly Studies · 2023
Typearticle
Languageen
FieldPsychology
TopicJungian Analytical Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsArchetypePassionTheme (computing)Isolation (microbiology)Ideal (ethics)Inheritance (genetic algorithm)HistoryPsychoanalysisSociologyGenealogyPhilosophyArtPsychologyLiteratureEpistemologySocial psychology

Abstract

fetched live from OpenAlex

The castaway archetype is examined in Lucy Irvine’s Castaway and Gerald Kingsland’s The Islander—dual accounts of a year spent on Tuin Island in the Torres Strait north of Australia. The castaway archetype adds a survivalist theme to C. G. Jung’s interest in living simply and close to nature—as he did at Bollingen—and intersects with his ideas in the essay “Archaic Man.” In general, castaways’ exposure to extreme isolation, survival conditions, and perils both physical and psychological activates an inheritance from ancient humans. However, contrasting markedly with Jung’s positive ideal in “Marriage as a Psychological Relationship,” Irvine and Kingsland live at cross-purposes because they constellate incompatible archetypes, which results in what Anthony Stevens calls the “frustration of archetypal intent.” Kingsland enacts the husband, but Irvine enacts the castaway; he loves her erotically, but her passion is for the island. Although projection, compensation, and enantiodromia complicate matters, the experience proves psychologically instructive for both, though the lessons are hard won.

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: Empirical · Consensus signal: none
Teacher disagreement score0.086
Threshold uncertainty score0.170

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0220.016
Scholarly communication0.0050.004
Open science0.0010.007
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0080.001

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 head0.083
GPT teacher head0.439
Teacher spread0.356 · 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
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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