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Record W2557657496 · doi:10.5539/jpl.v10n1p204

Evaluation of United Nations Security Council Resolutions by States

2016· article· en· W2557657496 on OpenAlexvenueno aff
Malihe Behfar, Hassan Savari

Bibliographic record

VenueJournal of Politics and Law · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSecurity councilPrinciple of legalityNoticePolitical scienceState (computer science)Public administrationMember statesInterpretation (philosophy)International securityLawInternational tradeEuropean unionBusinessComputer sciencePolitics

Abstract

fetched live from OpenAlex

In UNs history, the Legality of Security Council Resolutions, in many cases, is challenged. Generally, these challenges are taken by States that affected Security Council decisions. With notice that States are the representative for implementation of SCR, they intervene their determination and interpretation in the way that implement SC Decisions. In some cases, domestic and regional courts evaluate the state action in implementation SCRs. Although these cases couldn’t provide direct review on Resolutions, but affected in the way of implementation. Evaluation by States is probable and arises some concerns about decreased effectiveness of SC in the maintenance of international peace and security.

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)0.192
metaresearch head score (Gemma)0.361
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.192
Threshold uncertainty score0.997

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1920.361
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0120.012
Science and technology studies0.0040.003
Scholarly communication0.0090.006
Open science0.0020.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0140.002

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 head0.048
GPT teacher head0.331
Teacher spread0.283 · 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.

Study designObservational
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
Published2016
Admission routes1
Has abstractyes

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