Explaining Delayed Cessation: A Case Study of Rwandan Refugees in Zimbabwe
Bibliographic record
Abstract
Cessation is a process that removes refugee status. If cessation occurs too soon, it risks the lives of individuals sent back to their countries of origin. If cessation happens too slowly or not at all, states may become more reluctant to accept refugees in the first place. The most recent experiment in cessation is underway – and well behind schedule. Two deadlines recommended by the United Nations High Commissioner for Refugees for the cessation of refugee status of Rwandans have come and gone, yet some 100,000 Rwandan refugees remain in countries of asylum. This article hypothesizes that the delay to implementation of Rwandan cessation by many African states is driven by regional political concerns with irregular migration. Unilateral cessation may cause undesirable irregular migration, which poses a challenge for a region composed of states with varying levels of support for cessation and at various stages of implementation. Cessation is a state prerogative but may only work effectively as an act of regional consensus. Meanwhile, Rwandan refugees are faced with indefinite uncertainty about their legal status. Most Rwandan refugees have not experienced premature cessation, but delayed cessation. If coordinated implementation of cessation does not occur, the outstanding Rwandan refugee population will dwindle slowly over time, primarily because individuals opt for voluntary return or host states increase local integration. As delays mount in implementation and enforcement of the ceased circumstances clauses, one must conclude that the UNHCR advisory deadlines for cessation were premature, or that cessation has not proved as effective as the 1951 Refugee Convention intended – or both.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".