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Record W80596856 · doi:10.21236/ada594198

The Future of Russia and the Russian Navy. Report of Discussions in Moscow November 2-6, 2003

2004· report· en· W80596856 on OpenAlexaboutno aff
H. H. Gaffney, Dmitry Gorenburg

Bibliographic record

Venuenot available
Typereport
Languageen
FieldSocial Sciences
TopicRussia and Soviet political economy
Canadian institutionsnot available
Fundersnot available
KeywordsNavyPolitical scienceAeronauticsLibrary scienceEngineeringComputer scienceLaw

Abstract

fetched live from OpenAlex

Abstract : As part of CNAC s continuing project on the future of U.S.-Russian naval cooperation, Drs. Gaffney and Gorenburg paid a short visit to Moscow to discuss the future of the Russian Federation Navy (RFN). Mindful of the discretion required because of the Igor Sutyagin case (Sutyagin worked closely with us at CNAC in past years, but under the auspices of Dr. Sergey Rogov and the Institute for USA and Canada Studies of the Russian Academy of Sciences), we took an informal, unofficial, and top-down approach to discussing this subject, not pressing for any details about the RFN. The top-down approach is to ask first where Russia is going in its governance, politics, economy, and in constructing a social contract to replace that of Soviet times. Then, the question becomes what kind of budget and budget restraints the government may be under, what that may leave for the regular military establishment (they refer to it as the Army ), and finally, what would be left of that for the Navy. This approach is in contrast to what some consider a standard approach: what are the national interests, what are the threats to those national interests, what strategy is appropriate to cope with the threats in defense of the interests, what forces then are required for the strategy, and then to wallow in despair because there s never enough money to satisfy the requirements, especially if your country has a market economy and a government budget dependent on tax revenues. Russians including some we talked to have done a lot of work in accordance with this latter approach and it has essentially yielded much paper and little else mostly because the Russian economy has been in such bad shape that there s no money. Besides, the two wars in Chechnya have been a large drain in both resources and the Russian psyche. In any event, Russian armed forces, including the navy, continue practically unreformed (from Cold War days) and are still in decline.

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)0.002
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0080.002
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0120.003

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 head0.318
Teacher spread0.302 · 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
Published2004
Admission routes1
Has abstractyes

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