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Record W2918642414

16세기 지중해 향신료 무역

2010· article· ko· W2918642414 on OpenAlexaboutno aff
남종국

Bibliographic record

Venue서양중세사연구 · 2010
Typearticle
Languageko
FieldSocial Sciences
TopicGlobal Maritime and Colonial Histories
Canadian institutionsnot available
Fundersnot available
KeywordsPortugueseProsperitySpiceHistoriographyQuarter (Canadian coin)MonopolyHistoryPort (circuit theory)Economic historyGeographyPolitical scienceEconomicsLawArchaeologyEngineeringPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Vasco Da Gama`s fleet dropped anchor at the port of Calicut on 20 may 1498. It was Venice that came firstly to know this alarming news. The opening of a new route to India, land of spices, appears of the greatest importance to Venice since venetian prosperity depended on levantine spice trade. Until recently, many historians debate whether the Portuguese reduced the levantine spice trade to permanent insignificance. This article aims to make a good synthesis of the historiography of the rise and fall of levantine spice trade in the sixteenth century after the Portuguese opened their new route to India. In the first place, it is generally accepted that levantine spice trade reduced considerably in the first quarter of the sixteenth century. And then, levantine spice trade began to revive. That revival is not only quantitatively but also qualitatively attested by divers texts such as commercial documents, official records, diary, and so on. In the 1570s levantine spice trade a gain suffered from the revival of the portuguese indian spice trade. But this crisis was of a short-term fluctuation in the general economic situation. Finally, the Portuguese could not succeed in establishing a permanent monopoly in the import to Europe of oriental spices in the sixteenth century, conceding to new comers to India, Netherlands and England that gave a final blow to the levantine spice trade in the seventeenth century.

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)0.000
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: Other · Consensus signal: Other
Teacher disagreement score0.126
Threshold uncertainty score0.420

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1260.019

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 head0.009
GPT teacher head0.264
Teacher spread0.256 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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