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Record W2954745020 · doi:10.1177/0843871419844875

Channelling violence at sea: States, international trade and the transformation of naval forces from the high Middle Ages to the age of steam

2019· article· en· W2954745020 on OpenAlexaff
Richard W. Unger

Bibliographic record

VenueInternational Journal of Maritime History · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMaritime Security and History
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPoliticsAdversaryGovernment (linguistics)Executive branchPolitical scienceEconomic historyEconomyInternational tradeHistoryEconomicsLaw

Abstract

fetched live from OpenAlex

Certain late medieval changes in government practices, influenced by political developments and technological changes at sea, led to increasing limitations to acts of violence on European oceans and seas. The motivation of states became more overtly economic through the sixteenth and early seventeenth centuries. From around 1650 the expansion in trade, and most especially long distance trade, led to changes in the role, composition and size of naval forces. By the first decades of the nineteenth century nations directed their navies and violence at sea in general at protecting domestic commerce and disrupting that of any enemy.

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)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.010
Scholarly communication0.0050.003
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.231
Teacher spread0.217 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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