<i>“It Has Been a Breath of Fresh Air”</i> Participants’ Perception of the Impact of Self-Reliance Intervention Among Urban Refugees
Bibliographic record
Abstract
The forcibly displaced population is at a record high, including over 35 million refugees. Most refugees are hosted in neighboring countries where they face limited opportunities to rebuild their lives and they often remain in protracted displacement for an average of 20 years. The traditional durable solutions namely - repatriation, resettlement and local integration, are increasingly scarce, with only four percent of refugees achieving any of these outcomes recently. Humanitarian efforts are overwhelmed, making the prevailing response to refugee crises inadequate. Developing innovative and sustainable support strategies is imperative. Thus, in recent years, a solution to the plight of refugees has focused on the development of self-reliance intervention. Guided by the Amartya Sen Capabilities Approach and the Kalobeyei Conceptual Framework for Refugee Self-Reliance, this qualitative study explored the impact the Urban Refugee Protection Program (URPP), a self-reliance intervention program, on participant well-being. Data was collected through in-depth interviews with 23 refugees from various African countries during the summer of 2023 (June-August). The study identifies five themes: economic empowerment through holistic approach, capacity building, emotional and social benefits, and challenges faced by refugees. The findings emphasize the importance of self-reliance interventions as a critical solution to the global refugee crisis. The holistic approach addresses multiple dimensions of well-being, helping refugees transition from survival to self-sufficiency, restoring their dignity and hope for the future. Implications for practice, policy and research are discussed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".