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Record W4386481720 · doi:10.1093/jcsl/krad012

The practice of non-recognition and economic sanctions: The case study of Ukraine, Manchuria and South Africa

2023· article· en· W4386481720 on OpenAlexaff
Quoc Tan Trung Nguyen

Bibliographic record

VenueJournal of Conflict and Security Law · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Sanctions and International Relations
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsSanctionsEconomic sanctionsPolitical scienceLegal practiceLaw

Abstract

fetched live from OpenAlex

Abstract Russia’s invasion of Ukraine again proves the essential role of collective non-recognition against unlawful situations, despite contentious debates concerning the status of the principle of non-recognition in the international legal system. However, an old but unsettled question also resurfaces: How should we perceive the relationship between the practice of non-recognition and economic sanctions? Some scholars contend that the practice of non-recognition must entail economic punishments and isolation and be ‘as oppressive as possible’. For quite some time, the economic language has dominated as the only language that matters facing any unlawful situations. Unfortunately, this tendency also undermines a long history of innovative development of other forms of declaratory and institutional non-recognition. This article, by examining the current non-recognition campaign in the case of Ukraine, together with case studies of Manchuria and of the South African apartheid regime, endeavours to provide more perspectives concerning the relationship between non-recognition and economic sanctions. The article argues that, compared to other forms of non-recognition, economic sanctions have never been a reliable factor in demonstrating international attitudes and the legal beliefs of the international community.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.053
GPT teacher head0.275
Teacher spread0.222 · 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
Published2023
Admission routes1
Has abstractyes

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