Alleviation of Refugees COVID-19 Pandemic Risks- A Framework for Uncertainty Mitigation
Bibliographic record
Abstract
The impact of COVID-19 pandemic on the refugees has been a global concern where the possibility of its impact on the total life and livelihood is expected to be tremendous; unless drastic intervention programs are deployed in time of disaster. This paper explores the three largest most vulnerable refugee groups facing the pandemic of COVID-19. The work was approached from a multidisciplinary perspective with the aim of observing the topic from various mindsets such as economy, social science, history, and culture so that a holistic solution can be proposed. Refugees’ variables of uncertainty are examined during both the literature review and the case study. Then the formula of uncertainty is developed, based on the synthesis of both the cases and literature. The uncertainty is then mitigated and eliminated while talking about the risks of the COVID-19, and its potential spread. Finally, a generic framework is proposed so that the refugees not only are protected, but believe that they can have alternative solutions as they come out of the crisis. The paper brings in lots of implications to the international funding agencies, the refugees hosting countries and the local NGOs in the ground; beside the refugees themselves.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| 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".