Shared Heuristics: How Organizational Culture Shapes Asylum Policy
Bibliographic record
Abstract
What explains cross-national variation in asylum recognition rates? This is an important question that has implications for international law, public administration, and judicial politics. In many countries, bureaucratic agencies dominate the quasi-judicial process through which asylum-seekers are granted protective status (also known as refugee status determination or RSD). Refugee policy is unique in that it is the only form of migration policy that is codified into international law, Moreover, the United Nations High Commissioner actively monitors and guides implementation across the world. My study focuses on explaining developed countries with consistently high or low recognition rates. Comparing countries with varying degrees of procedural rights, influential refugee advocates, and experience hosting immigrants, my dissertation identifies the role of entrenched beliefs about asylum-seekers that stem from professional experience as the driver high recognition rates in Canada and of low recognition rates in Ireland, Japan, and South Korea. In this way, my study challenges conventional political science explanations of asylum policy that focus on international norms, political incentives, or the institutional rules of refugee status determination (RSD) procedures. Furthermore, it uses a mixed methods approach to both identify bureaucratic culture and test its effects as well as to illustrate that bureaucratic culture is not a reflection of public attitudes toward refugees.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".