Minatory Monsters for Turbulent Times: “The devil in the shape of a great fish” that presaged the English Civil War and other piscatorial prodigies
Bibliographic record
Abstract
Monsters, by the Latin definition of their name, are omens that portend turbulent times. The pamphlet A Relation of a terrible Monster called a Toad-fish, published in London in 1642, told of “a fiend, not a fish; at the least a monster, not an ordinary creature” which had become entangled in a fishing net and then put on display in London. The creature was described as resembling a giant toad, with a wide, toothy mouth and human characteristics of ribs, hands, and fingers. Discovery of the Thames monster instilled a sense of worry throughout the realm. The landing of the “Toad-fish” was linked in the tract to a bloody encounter that occurred between two well-known members of the British aristocracy fighting on opposing sides at the onset of the Civil War. The present paper describes how this vernacular publication was part of a flourishing of providential pamphlets in the 17th century wherein natural anomalies were invested with wider ecclesiastical and political meaning. Also undertaken herein is a review of various candidate species from which to suggest that the mysterious Toad-fish may have been another example of the angelshark’s (Squatina squatina) monstrous alter ego. This is an animal that has previously been suggested as being responsible for the ‘sea monk’ noted in several prominent natural histories of the Renaissance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".