Where is the Policy in Refugee Studies? Enhancing the Role of Policy Studies in the Study of Refugee Policy
Bibliographic record
Abstract
Abstract Refugee and forced migration studies have long drawn on a range of disciplinary insights, yet the field of policy studies remains under-utilised. This article explores the intersection between public policy and refugee studies, arguing that greater engagement with policy studies can enrich the analysis of refugee responses. While research on refugee policy has expanded in recent years, most of this scholarship does not employ established theoretical or analytical tools from policy studies. As a result, many studies overlook how broader policy processes – shaped by actors, interests, and institutions – affect the adoption, evolution, and implementation of refugee policies. To address this gap, we propose the use of the public policy cycle model for advancing research, teaching, and analysis within refugee studies. The model highlights distinct stages in the policy process, providing entry points for researchers to engage with how refugee policies are made, contested, and institutionalised. Importantly, the framework is applicable at both domestic and international levels, making it a valuable tool for analysing national refugee responses as well as developments in global refugee policy, which remains understudied. By integrating policy studies more systematically, the field of refugee studies can better explain how refugee policies emerge and evolve across different contexts.
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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 | 0.169 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.016 | 0.045 |
| Scholarly communication | 0.034 | 0.040 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".