Rural Depopulation and the Migration Turnaround in Mediterranean Western Europe: A Case Study of Aragon
Bibliographic record
Abstract
We have selected Aragon in the northeast of Spain as a long-run case study for the problem of rural depopulation in Mediterranean Western Europe. The strength and persistence of the depopulation in the region has left numerous rural districts in extreme situations of low demographic density. The basic cause of this phenomenon is the intensity of rural-to-urban migratory processes in the Aragonese countryside. Rural depopulation in Aragon has not yet stopped. However, important changes have taken place since the 1990s. In the first place, migration has been replaced by negative natural growth as the key factor in rural depopulation. Furthermore, the current situation features a reversal of the migratory balance, resulting in a sharp deceleration in depopulation since 2001 and positive growth in the larger country towns. This switch in migratory flows is partly due to the arrival of foreign-born immigrants, who are attracted by opportunities arising as a result of the difficulty of replacing the active population. At the same time, Aragon is close to the top of the ranking of Spanish regions in terms of per capita income, while an incipient process of restructuring and change has begun in the rural hinterland and the emergence of new residential and tourist functions has helped attract Spanish urban migrants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".