What Influences the Use of Data on Irregular Migration Flows in Public Debates and Policy Making? Findings from Strategic Case Studies.
Bibliographic record
Abstract
This working paper explores factors that influence the presence of irregular migration flow data in public debates and policy decision making. Observations from five strategic case studies exploring the data on selected patterns of irregular migration flows in different national contexts are compared: visa applications and overstaying data in Spain, irregular border crossing data in Greece, data documenting irregular migration status change in registration data in the Netherlands, data on the administration of asylum procedures in Germany and data on temporarily admitted migrants who fall out of status due to administrative dysfunction in Canada. As cross case unit of analysis, the multiple case study pursues the question of how selected factors influence the presence and use of irregular migration flow data in public debates and policy decision making. The explorative analysis suggests that – in addition to availability, accessibility, and quality – data expediency is a strong factor influencing the presence and use of irregular migration flow data in public debates and policy decision making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".