Bibliographic record
Abstract
This study reviews the governance of Kampala’s food system and refugee protection approach in order to propose strategies to recognize and protect the food security needs of Kampala’s refugee population more effectively. Uganda is Africa’s largest refugee host, with a policy approach that has been widely lauded for its flexible settlement provisions and commitment to durable solutions. However, growing refugee populations and underfunding have led to serious pressures, severely exacerbated during the COVID-19 pandemic. One unique aspect of Uganda’s refugee governance approach is the allowance of refugee populations to self-settle outside of designated camps in the capital city, Kampala. This research uses a governance lens to explore what is being done to support the food security of this group, by whom, and how this could be improved. The researcher conducted interviews with asylum seekers and refugees living in two of Kampala’s large informal settlements (Kisenyi II and Namuwongo) and with a range of policy stakeholders during May 2023. Multiple levels of government and non-governmental organizations (NGOs) offer overlapping formal and informal services and programs accessible to different populations living in settlements. This paper points to gaps and limitations linked to resources, as well as difficulties identifying vulnerable populations, locating political responsibility, coordination, and weak policy implementation, and suggests governance strategies to respond better to refugee and asylum seekers’ food security needs. Key recommended responses are to overhaul the refugee registration system, recognize and protect urban food security, and improve policy actor coordination through collaborative strategies that move beyond awareness of the crisis to setting specific targets and timelines to address it.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".