International Migration and Rural Areas: Cross-National Comparative Perspectives
Bibliographic record
Abstract
While immigrants are still predominantly choosing urban areas to locate to, there is now increasing evidence of immigration to rural areas which poses its own challenges for those relocating, from the scarcity of high quality jobs to the provision of public and private services. Addressing the shortcomings in current research, this book employs an innovative approach by exploring this relationship from a cross-national, comparative, global perspective. It draws lessons from case studies across a range of geographical and political contexts, including Canada, the USA, Ireland, Scotland, Greece and Russia. Bringing together migration experts from a range of academic disciplines, International Migration and Rural Areas contributes to conceptual developments and also identifies policy concerns which can be pursued at national, sub-national and supra-national levels. As such, it will appeal to policy makers, as well as scholars across a range of disciplines, including geography, politics, demography, social policy, sociology and anthropology.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".