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Record W4399715563 · doi:10.56976/jsom.v3i2.78

NATO Enlargement and the US- Russian Relations in the 21st Century: A Critical Analysis

2024· article· en· W4399715563 on OpenAlexaff
Sufyan Akhlaq, Muhammad Arslan, Qasim Shahzad Gill, Ghulam Mustafa

Bibliographic record

VenueJournal of Social & Organizational Matters · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Relations and Foreign Policy
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsResizingPolitical scienceInternational tradeEconomicsEuropean union

Abstract

fetched live from OpenAlex

Cold War was the rivalry between the Soviet Union and the United States to expand their ideology and influence worldwide to become the most powerful state of the world. A significant element of the Cold War was the making of alliances by both the United States and the Soviet Union to materialize their objectives and to contain each other’s ballooning influence and ideology. The weakening position of the Soviet Union brought the two countries closer. In addition to this, the United States' assurances to the Soviet Union about restricting NATO’s borders normalized relations between the two rivals after being engaged in a Cold War for over 40 years. Later, the disintegration of the Soviet Union in the early 1990s prompted the world to believe that the Cold War had come to an end. However, the North Atlantic Treaty’s inclusion of more European countries backed by the United States has again reinstated the Russia-US rivalry leading to the Russian invasion of Ukraine. This paper delves into the role of the North Atlantic Treaty Organization during the Cold War era. The paper then tries to figure out whether there is any significant proof of American promises to the Soviet Union regarding the restriction of NATO’s borders. The paper then analyzes Russian policy in the 21st century and its invasions of Ukraine in the context of American promises.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.953
Threshold uncertainty score0.851

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.308
Teacher spread0.301 · 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 teacher head, 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

Citations0
Published2024
Admission routes1
Has abstractyes

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