Bibliographic record
Abstract
Popular interest in cryptozoology (the study of unconfirmed species, such as bigfoot and chupacabra) has been fuelled by a recent publishing frenzy of encyclopaedias, dictionaries, and guides devoted to the subject, as well as by unprecedented opportunities for enthusiasts to collect data and exchange stories via the Internet. The author situates the emotional commitment many exhibit toward cryptids (the creatures themselves) in a broad historical context. Unconfirmed species served as an implicit ground of conflict and dialogue between untutored masses and educated elite, even prior to the rise of academic science as a unified body of expert consensus. The psychological significance of cryptozoology in the modern world has new facets, however: it now serves to channel guilt over the decimation of species and destruction of the natural habitat; to recapture a sense of mysticism and danger in a world now perceived as fully charted and over-explored; and to articulate resentment of and defiance against a scientific community perceived as monopolising the pool of culturally acceptable beliefs. “Man, it is true, can, by combination, surmount all his real enemies, and become master of the whole of animal creation: But does he not immediately raise up to himself imaginary enemies, the dæmons of his fancy …?”—David Hume (Smith 1947 Smith, Norman Kemp, ed. 1947. David Hume, Dialogues Concerning Natural Religion, New York: Macmillan. 1779 [Google Scholar], 195).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".