Bibliographic record
Abstract
Cryptozoology, the pursuit of wildlife ignored or discounted by mainstream zoology, emerged as a separate discipline from zoology in 1955 with the publication of Bernard Heuvalmans’ book On the Track of Unknown Animals. Although it is typically associated with pseudoscience, many of the discipline’s advocates assert that cryptozoology should be recognized as a legitimate science. This has proven difficult because of the nature of the discipline and its inability to provide falsifiable evidence. This paper examines crytozoology’s dichotomous separation from zoology; its search for hard evidence to support the existence of obscure creatures including hominids, sea serpents and lake monsters; and its efforts to document in a clear and objective way the existence of such creatures so as to distance itself from paracryptozoology as well as both the media and public’s distorted understanding of the field. This paper argues that by its nature cryptozoology is bound to remain, at worst, a pseudoscience and, at best, a transitional field of research. The example of the discovery of creatures like the giant squid, which left the realm of mythology and became a recognised species of zoology in 2004, provides evidence of both the promises and the inherent problems of the field of cryptozoology.
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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.098 |
| Scholarly communication | 0.020 | 0.029 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".