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Record W2159143632 · doi:10.1111/0591-2385.00392

Imagination and Reality: On the Relations Between Myth, Consciousness, and the Quantum Sea

2001· article· en· W2159143632 on OpenAlexaff
Charles D. Laughlin, C. Jason Throop

Bibliographic record

VenueZygon® · 2001
Typearticle
Languageen
FieldPsychology
TopicParanormal Experiences and Beliefs
Canadian institutionsCarleton University
Fundersnot available
KeywordsMythologyConsciousnessEpistemologyRelation (database)Function (biology)Field (mathematics)AestheticsPhilosophySociologyLiteratureArtComputer scienceMathematics

Abstract

fetched live from OpenAlex

There often appears to be a striking correspondence between mythic stories and aspects of reality. We will examine the processes of creative imagination within a neurobiological frame and suggest a theory that may explain the functions of myth in relation to the hidden aspects of reality. Myth is peppered with archetypal entities and interactions that operate to reveal hidden processes in reality that are relative to the human condition. The imagery in myths in a sense “sustains the true.” That is, mythopoetic imagery keeps the interpretive process in experience closer to the actual nature of reality than the rational faculties operating alone are able to do. Indeed, whereas rationalizing can easily lead us awry, genuine myth rarely does. Explanations of events offered by cultures around the world are frequently couched in terms of mythic themes and events. An important function of myth is to provide a “field of tropes” that in‐forms the lived experience of people. This paper focuses especially on those aspects of myth that represent facets of the quantum universe and give us clues as to the relationship between consciousness, symbolism, and reality.

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)0.005
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 score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.049
Scholarly communication0.0060.010
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.038
GPT teacher head0.331
Teacher spread0.293 · 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

Citations30
Published2001
Admission routes1
Has abstractyes

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