Refugee Resettlement Scheme is the Golden Ticket but Very Scarce for Many in Africa: Prospects and Challenges During COVID-19
Bibliographic record
Abstract
The refugee resettlement scheme is a window of hope for millions of refugees across the globe. It is an opportunity that grants refugees a durable solution, especially those who cannot voluntarily return to their countries of origin due to fear of persecution. The golden ticket, as seen by many, is resettlement to Europe, the United States, Canada, Australia, and New Zealand. COVID-19, the reluctance by western countries to commit to responsibility-sharing, along with other constraints confronting the process of granting settlement to refugees generated barriers to resettlement in 2020. Resettlement opportunities have been scarce for many refugees in Africa, particularly those refugees with protracted cases. The consequences are reflected in an increase in the use of irregular migration routes to Europe rather than relying on the established resettlement scheme. This paper aims to provide an overview of the options for a durable solution, exploring and discussing the prospects and challenges during the era of COVID-19. In conclusion, the data derived from the literature review and 12 key informant interviews suggests that the impact of COVID-19 on the resettlement scheme and the implementation of Africa’s first free trade area that promises to increase human mobility provides an opportunity for the international community to reimagine the golden ticket. Supported by the Comprehensive Refugee Response Framework (CRRF) and the Global Compact on Refugees, local integration and resettlement within Africa should be the new achievable gold standard.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".