The practice of non-recognition and economic sanctions: The case study of Ukraine, Manchuria and South Africa
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".