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Record W4400493110 · doi:10.1093/ia/iiae150

NATO: from Cold War to Ukraine

2024· article· en· W4400493110 on OpenAlexaff
Marion Messmer, Stéfanie von Hlatky, Somdeep Sen, Trevor McCrisken, Liselotte Odgaard, Sten Rynning

Bibliographic record

VenueInternational Affairs · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean and Russian Geopolitical Military Strategies
Canadian institutionsQueen's University
Fundersnot available
KeywordsCold warPolitical scienceEconomic historyAncient historyHistoryLawPolitics

Abstract

fetched live from OpenAlex

Sten Rynning's new book provides a comprehensive history of why and how NATO was founded. The book outlines and navigates the challenges that the alliance has faced over the decades, bringing the analysis up to 2024, two years into Russia's war of aggression against Ukraine. Rynning's main argument is that NATO is at its best when it focuses on its core role: a defensive alliance and a guarantor of security for Europe. Rynning is a professor at the University of Southern Denmark, and one of the foremost NATO experts writing about the alliance today. He can draw on a long list of peer-reviewed journal articles and books that cover various aspects of NATO's history and its contemporary policy challenges. Rynning has also engaged with policy-makers throughout his career, and this is reflected in the target audience for his well-researched history of NATO. Indeed, he provides policy-makers with some suggestions on how member states can address some of today's challenges.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.004
Scholarly communication0.0060.003
Open science0.0000.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.014
GPT teacher head0.306
Teacher spread0.292 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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