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Record W4304996048 · doi:10.21463/shima.161

Minatory Monsters for Turbulent Times: “The devil in the shape of a great fish” that presaged the English Civil War and other piscatorial prodigies

2022· article· en· W4304996048 on OpenAlexfundno aff
Robert France

Bibliographic record

VenueShima · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYersinia bacterium, plague, ectoparasites research
Canadian institutionsnot available
FundersDalhousie University
KeywordsMonsterVernacularHistoryLiteratureArt

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.008
Scholarly communication0.0050.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.019
GPT teacher head0.261
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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