Legal Refugee Recognition in the Urban South: Formal v. de Facto Refugee Status
Bibliographic record
Abstract
The legal relevance of the “urban refugee” concept in the Middle East and Africa stems from the practice of practicing different forms of refugee status determination (RSD) in rural as opposed to urban areas. Urban refugees are usually subject to rigorous individual adjudication, while rural refugees are typically recognized on a prima facie basis. This difference in procedure has no basis in the substance of refugee law, and it marginalizes urban refugees in two key ways. First, in Africa and the Middle East, refugee status recognition is used by host governments to prevent refugee integration, to force refugees to live far from population centres, and to transfer responsibility for their welfare to international agencies. Second, individualized RSD procedures in wide use by the United Nations generally lack key fairness safeguards, increasing the risk that genuine refugees will be wrongfully rejected. This phenomenon means that urban refugee populations will often be systematically undercounted, and will include a significant number of de facto refugees who are in fact refugees in danger of refoulement, but whose applications were rejected and who thus have no access to the protection and resources otherwise targeted at refugees.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".