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Record W2885159556 · doi:10.1017/s000708741800050x

Reading and writing the scientific voyage: FitzRoy, Darwin and John Clunies Ross

2018· article· en· W2885159556 on OpenAlexaff
Katharine Anderson

Bibliographic record

VenueThe British Journal for the History of Science · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Science and Natural History
Canadian institutionsYork University
Fundersnot available
KeywordsDarwin (ADL)AppropriationCasualReading (process)NarrativePortraitAstronomerLiteratureHistoryArt historySociologyClassicsPhilosophyArtEpistemologyLawComputer science

Abstract

fetched live from OpenAlex

An unpublished satirical work, written c.1848-1854, provides fresh insight into the most famous scientific voyage of the nineteenth century. John Clunies Ross, settler of Cocos-Keeling - which HMS Beagle visited in April 1836 - felt that Robert FitzRoy and Charles Darwin had 'depreciated' the atoll on which he and his family had settled a decade earlier. Producing a mock 'supplement' to a new edition of FitzRoy's Narrative, Ross criticized their science and their casual appropriation of local knowledge. Ross's virtually unknown work is intriguing not only for its glimpse of the Beagle voyage, but also as a self-portrait of an imperial scientific reader. An experienced merchant seaman and trader-entrepreneur with decades of experience in the region, Ross had a very different perspective from that of FitzRoy or Darwin. Yet he shared many of their assumptions about the importance of natural knowledge, embracing it as part of his own imperial projects. Showing the global reach of print culture, he used editing and revision as satirical weapons, insisting on his right to participate as both reader and author in scientific debate.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.012
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.042
GPT teacher head0.253
Teacher spread0.211 · 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.

Study designQualitative
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

Citations34
Published2018
Admission routes1
Has abstractyes

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