Deprivation of citizenship and creation of statelessness: case study of Belarus
Bibliographic record
Abstract
This research paper explores the use of citizenship deprivation and trials in absentia as tools of political repression, with a particular focus on Belarus. In Belarus, an amendment to the citizenship law was passed on July 11, 2023 allowing the Belarusian president to revoke Belarusian citizenship of those abroad, even if it would render them stateless. This new amendment would also allow for trials to be conducted in absentia, with a possible sentence being citizenship deprivation. The paper also delves into the bureaucratic tactics used by the Belarusian government to exacerbate statelessness. These include the closure of consulates abroad and the requirement for citizens to be physically present in Belarus to renew their passports, making it nearly impossible for those in exile to maintain their citizenship. This leaves those in exile with minimal, if any, legal protections and access to basic rights. Additionally, the research looks at the role of international relations in exacerbating the arbitrary use of these practices, particularly in the context of the war in Ukraine. This conflict has intensified political repression in Belarus and prompted neighbouring countries, such as Lithuania and Poland, to introduce humanitarian visas to protect Belarusians who fled the country. By analyzing these developments, the paper highlights the devastating effects of the powers of citizenship deprivation, including the creation of stateless individuals through both legal and administrative means, and emphasizes the need for a coordinated international response to safeguard citizenship rights in these instances.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".