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Record W1853535832

Don't Expect Much from Japan in the Indian Ocean

2011· article· en· W1853535832 on OpenAlexvenueno aff
Toshi Yoshihara, J. Albert Holmes

Bibliographic record

VenueJournal of military and strategic studies · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicMaritime Security and History
Canadian institutionsnot available
Fundersnot available
KeywordsChristian ministrySolidarityDozenPolitical scienceMinistry of Foreign AffairsBusinessGeographyAeronauticsPublic administrationEngineeringPoliticsLaw
DOInot available

Abstract

fetched live from OpenAlex

Japan is an Indian Ocean power of long standing. Ten years ago, in a post-9/11 show of solidarity with the United States and to exercise a more muscular foreign policy, Tokyo committed vessels of the Japan Maritime Self-Defense Force (JMSDF, or MSDF) to the coalition naval contingent supporting combat operations in Afghanistan. JMSDF tankers resupplied coalition warships, while Aegis destroyers guarded against air and surface threats in the Arabian Sea. Japanese seamen posted impressive statistics for this naval enterprise. The Japan Ministry of Defense reported that JMSDF vessels supplied about 137 million gallons of fuel oil and some 2.8 gallons of water to customers from about a dozen countries, including the United States, Pakistan, France, Britain, and Germany. Tokyo spent over $110 million on the logistics mission in its final two years according to Defense Ministry spokesmen, even as demand for such support dwindled.2

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0110.007
Scholarly communication0.0070.008
Open science0.0010.003
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0200.008

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 head0.086
GPT teacher head0.296
Teacher spread0.210 · 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 designTheoretical or conceptual
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
Published2011
Admission routes1
Has abstractyes

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