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Record W4379656114 · doi:10.54254/2753-7048/4/20220149

How Should the United Nations Security Council Respond to Changing Balance of Power?

2023· article· en· W4379656114 on OpenAlexaff
Jing Zhong, Chengyuan Yang, Wanying Sun, Liz Lyu, Yuanshu Huang

Bibliographic record

VenueLecture Notes in Education Psychology and Public Media · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDilemmaSecurity councilBalance (ability)Power (physics)SkepticismPolitical scienceWork (physics)CriticismLaw and economicsPublic administrationSociologyLawPoliticsEngineeringEpistemology

Abstract

fetched live from OpenAlex

Together with the United Nations’ achievements comes constant criticism. The Security Council, as one of the core organs of the UN, has been a center of contention. The lack of practical responses to the Ukraine crisis since 2014 invokes another round of skepticism about the Council’s functionality and demand for reform. This paper does not consider the current dilemma exceptional. In contrast, this is merely another example that illustrates the Council’s increasing incapability when encountering the changing balance of power. Therefore, this paper claims the Council needs a robust reform to re-adapt to the grand trend. The first part briefly introduces the Council’s history to illustrate some possible fundamental issues in the Council. The second part analyzes the Council’s contemporary dilemmas when encountering crises involving great powers, such as Ukraine. Finally, the paper evaluates the existing plans for reform and proposes a new direction for consideration. Even though the Council has been suffering from weakness recently, it is unlikely that the world can completely abandon it soon before finding a more appropriate replacement. Therefore, this paper’s work is an initiating framework for future reform. But more research and discussion are necessary for a more elaborate scheme for practice.

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)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation 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 score0.841
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.366
Teacher spread0.306 · 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 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
Published2023
Admission routes1
Has abstractyes

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