Bibliographic record
Abstract
Lake Monster Mysteries: Investigating the World's Most Elusive Creatures. By Benjamin Radford and Joe Nickell. (Lexington: University Press of Kentucky, 2006. Pp. 190, foreword, acknowledgments, introduction, maps, photographs, illustrations, appendices, notes, index. $24.95 cloth) The line between the study of folklore and the study of popular culture has long been blurred. Academic presses, evidently under pressure to produce revenue, have meanwhile been blurring another line, the line between academic and popular publishing, issuing cross-disciplinary works on catchy subjects that purport to be academic but are in fact brazen attempts to sell to a popular audience. Folklore studies seem particularly vulnerable to such exploitation. And the explosion of rigorous academic popular culture inquiry into genuine folkloric phenomena has watered down what the public, and perhaps even the academy, expects of folklore study. A good example of this trend is the work under review, Lake Monster Mysteries, by Benjamin Radford and Joe Nickell. Crossing the disciplines of history and folklore, the book purports to be a scholarly inquiry into cryptozoological phenomena, but it is no more than an old-fashioned monster hunt, with appropriate amounts of debunking thrown in. The result is closer to Lewis Carroll's The Hunting of the Snark ([1876]1995) than to a scholarly treatment, folkloric or historic. Touting themselves as hard-headed myth-busters, the authors poke holes in various traditions surrounding lake monsters, including the hoaxes that have been perpetrated at Scotland's Loch Ness and Canada's Lake George. Supposed sightings of lake monsters are typically explained as floating logs or eels or otters playfully swimming in line. There is even an appendix titled Eyewitness (Un) reliability. The authors boast of tiieir combination of historic archival research and folkloric fieldwork in the pursuit of various lake monsters. Yet their survey of scholarship relies primarily upon popular rather than academic sources, and these they merely digest rather than analyze and evaluate for scholarly soundness. Their supposedly intensive fieldwork often amounts to a week or so spent at the lakes in question. While their interviews with eyewitnesses at each locale are, no doubt, of some significance, their forays by air, water or shoreline to identify legendary monsters or their natural equivalents seem hasty and happenstantial. A notable exception is the chapter on Ogopogo, who haunts Canada's Lake Okanagan. More than the other chapters, it provides an in-depth analysis of the phenomenon from historic and folkloric perspectives, and it seeks to answer questions that such a book should address throughout: Why do cultures need a monster? Why are lakes convenient places for monster lairs? How do various cultures - Native and European - adopt and adapt such monsters? …
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".