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Introduction

2016· book-chapter· en· W4245418577 on OpenAlexaboutno aff
Jonathan Eacott

Bibliographic record

VenueUniversity of North Carolina Press eBooks · 2016
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTreasureAdventureCONQUESTHistoryEconomic historyAncient historyGeographyArt historyArchaeology

Abstract

fetched live from OpenAlex

“The discovery of an India” could do more, for better or worse, than anything else for the English people, wrote Captain Edward Wynne to his king, Charles I, in 1623. The world did not have one India, it had many awaiting English discovery. Wynne explained that the “low-Countrymen,” meaning the Dutch, “have made the Sea their India; and by that sole waie of Fishing, have raised themselves to such an unweildlie Treasure.” The Dutch had found their India near Newfoundland. Wynne was sure that Newfoundland itself would be England’s India. It was more southerly than England, and it had, he claimed, mild winters with rare snows. Its potential wealth most made Newfoundland an India. Still, that wealth could be risky. The newfound wealth of the Dutch, he wrote, might lead to “lazyness, the readie waie to povertie.” But the benefits were worth such a risk. Wynne explained that, through territory in America, Charles I’s harbors would fill with ships and merchants, and “their houses with outlandish Commodities.” The king’s “Dominions” would gain “infinite wealth,” and he would gain great “coffers” of “Treasure.” For Wynne and other imperial thinkers and adventurers in Elizabethan and early Stuart England, India was a set of variable and sometimes competing ideas about wealth, trade, wondrous commodities, and potential corruption, more than it was a place. During the seventeenth and eighteenth centuries, however, Britons came to see India much more as the subcontinent of Asia—a place of cultivation, manufacturing, conquest, and potential religious allies and converts—than as a set of transferable ideas. America the India and India the place might have many of the same potential benefits and dangers in common.1

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.185
Threshold uncertainty score0.264

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0070.004
Open science0.0020.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.8150.679

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 head0.030
GPT teacher head0.164
Teacher spread0.134 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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