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Record W4392345099 · doi:10.1080/21515581.2024.2319667

Is security still the chiefest enemy? The challenges and contradictions in European confidence- and security-building in the Cold War

2024· article· en· W4392345099 on OpenAlexaff
Thomas Hughes

Bibliographic record

VenueJournal of Trust Research · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Relations and Foreign Policy
Canadian institutionsUniversity of ManitobaMount Allison University
Fundersnot available
KeywordsAdversaryCold warPolitical scienceComputer securityComputer scienceLawPolitics

Abstract

fetched live from OpenAlex

The regime of Confidence- (and Security-) Building Measures (C(S)BMs) represented an effort to re-imagine Arms Control in Europe and reduce the possibility of unwanted escalation due to misunderstanding or misperception. The regime was first developed during the Cold War due to concerns about large-scale military exercises, and its ongoing importance has come into sharp relief given that NATO and Russia have increasingly engaged in similar manoeuvres. However, despite the C(S)BMs, military exercises represented a point of conflict between NATO and the Soviet Union, and there is little indication that the regime led to the development of confidence in the benign intent of other participants. What prevented this from occurring? This paper compares the theory and logic of confidence-building with the negotiations around the CSBMs, highlighting three primary points of discontinuity that undermined the ability of the regime to fully deliver on its potential. The competitive nature of negotiation about its terms resulted in incomplete transparency, the conflation of the concepts of ‘confidence’ and ‘security’ shifted the focus towards assessing an adversary’s military capability rather than intent, and the regime’s inflexibility meant that it did not account for technological changes that otherwise altered understanding of proximate threat.

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)0.020
metaresearch head score (Gemma)0.019
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 score0.021
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0060.048
Scholarly communication0.0210.013
Open science0.0010.008
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0020.000

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 head0.096
GPT teacher head0.426
Teacher spread0.330 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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