Bibliographic record
Abstract
Abstract This chapter outlines the purpose of the book, which is to show that there are many different forms for expressing the urge to fly. Special attention is given to stories whose main character(s) are endowed with the ability to fly, such as Peter Pan by J. M. Barrie. On the basis of strategies for interpreting stories in the context of major underlying concerns of their creators, it is argued that Peter Pan was partly crafted as a means for granting outward expression to some of the internal needs of the author who brought him into existence. Evidence is presented to support this assumption and thereby gain a foothold on decoding the meaning of flying fantasies. The ideas that emerge from considering the life of Barrie are then be extended to the study of the lives of others for whom various forms of flight were of special appeal. This exploration will lead to a deeper understanding of Carl Jung's vision of himself in space, Marc Chagall's striking canvases of levitated figures, and Marshall Herff Applewhite's tragically enacted fantasy of himself and his Heaven's Gate followers being lifted from Earth by a comet. The book will also consider the lives of less well-known figures, for example, a boy who devoted several years to the project of creating a flying machine, a man who flew in a lawn chair with weather balloons fastened to it, and a murderer awaiting execution who regularly dreamt of being rescued by a bird and taken to heaven.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.202 | 0.093 |
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".