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Record W4248145772 · doi:10.1057/978-1-137-01237-1_7

Naval Comparisons

2016· book-chapter· en· W4248145772 on OpenAlexaboutno aff
Christopher Martin

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2016
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMaritime Security and History
Canadian institutionsnot available
Fundersnot available
KeywordsNavyDominance (genetics)AeronauticsHistoryOperations researchFellGeographyEconomic historyEngineeringCartographyArchaeology

Abstract

fetched live from OpenAlex

It is a matter of fact that the size of the Royal Navy has contracted significantly since the end of the Second World War. The stark reality of this reduction in numbers is amply demonstrated by noting that in terms of destroyers and frigates, the workhorses of any modern fleet, the Royal Navy possessed 19 in 2010; in 1950 it had 280. A simple decline in numbers is not the entire picture however. The reduction in fleet size is linked to a variety of factors including technology, cost, shifts in global politics and the divesting of formal empire. Furthermore, every navy in the West has seen a general contraction; even the United States Navy (USN) is facing reductions to overall numbers. So, where does the Royal Navy sit amid the hierarchy of the world’s navies today following its fall from dominance? Counting hulls is a crude approach but it does at least provide some means of comparison. If all types of submarines are excluded, as the focus of this study is the surface fleet, the Royal Navy has 57 warships of all types. This compares with China 348, Russia 172, India 130, Japan 88, Brazil 68, France 65, Australia 32 and Canada 22.1 In crude numbers, this places the Royal Navy seventh in rank among these navies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.649
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0120.002

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 head0.032
GPT teacher head0.268
Teacher spread0.236 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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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