Sustainable Development and Livelihoods of Rohingya Refugees in Bangladesh: The Effects of COVID-19
Bibliographic record
Abstract
Bangladesh is one of the top refugee-hosting countries of the world and adversely affected by the COVID-19. This paper aims to identify how the COVID-19 pandemic affects the Rohingya refugee and expose the vulnerability that challenges SDGs. The study follows a system approach grounded on a sustainable development model and uses secondary sources of data. The study found that fragmented and random policies in refugee crisis management during the COVID-19 reveals the policy lacks structural fragility due to inadequate policy and programs. Besides, the limited number of health care, food, education, washing facilities, housing, and the utilization of inferior materials in camps put pressure on the refugee health, education, and well-being during COVID-19. It also reduces the monetary funds, which affects humanitarian support, and limits the aid to SDGs in refugee camps due to restrictive policies. Moreover, refugees' inability to include an inclusive social security system is far from existing social inequality. This paper calls for robust policies and programs with adequate funding for structural logistics and effective service delivery in refugee management for their future well-being and promoting SDGs in refugee camps.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".