From uncertainty to policy a guide to migration scenarios
Bibliographic record
Abstract
This unique book provides a practical and interdisciplinary blueprint for determining quantitative scenarios of future international migration. Focusing on complexity and uncertainty as the defining challenges of migration, it explores how scenario building can be used to inform and underpin effective migration policy and practice. Through conceptual, theoretical and methodological analysis, From Uncertainty to Policy: A Guide to Migration Scenarios outlines the current state of the art in future-oriented migration studies. Highlighting key lessons and recommendations, expert contributors assess both the opportunities and limitations of scenario building as an analytical device. They combine demographic, statistical, sociological, economic, geographic and political science expertise to develop a new multi-step process for estimating, predicting and simulating migration flows and patterns. Ultimately, the book emphasises the importance of accounting for uncertainty and complexity in migration policy and presents practical tools for accurately measuring and managing migration now and in the future. Advancing the methodology of setting migration scenarios under uncertainty, this book is an essential resource for migration practitioners, advisors and policy-makers and a valuable read for students and scholars of migration studies, geography and population sciences.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".