A crisis mode in migration governance: comparative and analytical insights
Bibliographic record
Abstract
This paper takes stock of the emerging literature on the governance and framing of both migration and asylum as 'crises'. This study carries forward this line of thinking by showing how the crisis governance of migration is not just a representation or a discourse but emerges as a mode of governance with specific features. The study focuses on the refugee emergency of 2015-2016, covering however a longer time frame (2011-2018) and a wide set of 11 countries (those neighbouring Syria: Lebanon, Iraq and Turkey; countries that were mainly transit points: Greece, Italy, Poland and Hungary; and countries that were mainly destination points (Austria, Germany, Sweden and the UK). Through the meta-analysis of a broad set of materials arising out of the RESPOND research project, we identified three interacting governance features in times of crisis. These include (1) a multilevel but complex actor landscape (2) complicated and fragmented legal systems and policy provisions that may vary both at the temporal and territorial level; (3) a renationalisation narrative that seeks to bring this multifaceted and fragmented governance landscape together under the promise that the national state can re-establish control and solve the 'crisis.'
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.005 |
| 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".