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Record W2766433014

Pirates and Prosthetics: Manly Messages for Managing Limb Loss in Victorian and Edwardian Adventure Narratives

2018· book-chapter· en· W2766433014 on OpenAlexaboutno aff
Ryan Sweet

Bibliographic record

VenueWhite Rose Research Online (University of Leeds, The University of Sheffield, University of York) · 2018
Typebook-chapter
Languageen
FieldArts and Humanities
TopicShort Stories in Global Literature
Canadian institutionsnot available
Fundersnot available
KeywordsAdventureNarrativeHistoryArtVisual artsArt historyLiterature
DOInot available

Abstract

fetched live from OpenAlex

Many of us associate pirates with prosthetic body parts. From wooden legs to hook hands, prostheses have frequently appeared in imaginative representations of pirates, such as Captain Hook from J. M. Barrie's 1904 play Peter Pan, Captain Barbosa and Ragetti from the Pirates of the Caribbean (2003–11) film series, the badges of the sports teams the Cornish Pirates and Pittsburgh Pirates, and the products and branding of the Woodenhand Brewery in Truro, Cornwall. Yet this prevalent association has not always existed. Its literary history is, in fact, curious. What we might consider the great age of pirate stories (c. 1858–1904) exhibits relatively few prosthesis-using characters, aside, of course, from one obvious example: Captain Hook. What we do, however, see in the fiction from this period, and what we today unthinkingly assume are wooden leg users, are a number of pirates who persevere with their deplorable duties in spite of disability.\n\nThe second quotation above, from Robert Michael Ballantyne's 1883 novel The Madman and the Pirate, exposes a rare example of a fictional pirate from this period who does use wooden legs – though it should be noted that this character, Captain Rosco, only loses his legs and begins wearing prosthetic replacements after his piratical career has ended.

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)0.003
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.123
Threshold uncertainty score0.245

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0230.024
Scholarly communication0.0100.004
Open science0.0020.011
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.031
GPT teacher head0.239
Teacher spread0.208 · 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
GenreOther

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

Citations2
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueWhite Rose Research Online (University of Leeds, The University of Sheffield, University of York)Same topicShort Stories in Global LiteratureFrench-language works237,207