Bibliographic record
Abstract
Abstract Fantasies of Flight invigorates the field of personality psychology by challenging the contemporary academic view that individuals are best studied as carriers of traits. The book exchanges a heart-to-heart, case study approach to understanding human behavior for the current strategies of categorizing and comparing individuals according to their manifest traits. Its author asks and endeavors to answer questions like “What were the psychological conditions that led Sir James Barrie to create a character named Peter Pan?” and “What were the dynamics behind the Marshall Herff Applewhite's conviction that a space ship, hiding behind the Hale–Bopp comet, would rescue him and his Heaven's Gate followers after they enacted a mass suicide pact in 1997?” Answering these questions requires the author to resurrect “old” ways of thinking about personality and “old” strategies for studying individuals one by one. Early in the book, the author reviews the history of why intensive case studies were discredited in psychology and describes how Sigmund Freud's psychobiographical account of Leonardo da Vinci's fascination with flight inadvertently abetted critics of psychoanalytic psychology. He then performs a partial psychobiography of James Barrie and the origins of Peter Pan, followed by an investigation of Carl Jung, who fashioned the collective unconscious to serve as humankind's link with eternity. Arguing that personality psychology needs to become less insular, the author integrates information from the disciplines of developmental psychology and neuroscience into a theory regarding the latent needs that both Barrie and Jung sought to satisfy. The theory, including its emphasis on the onset of self and consciousness, is then applied to an array of well-known and obscure individuals with ascensionistic inclinations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".