Asylum Seekers in Small Villages: Spatial Proximity and Integration in Italian and French Villages
Bibliographic record
Abstract
What happens when asylum seekers from African or Middle Eastern countries are resettled by authorities in small European villages? When they arrive, are they welcomed, or on the contrary, rejected by villagers? Generally speaking, overseas migrants usually wish to be resettled in large European cities. As for European villagers, they tend to form communities closed on themselves, so one might expect a rather cold reception. However, fieldwork in Italian and French villages where asylum-seeking migrants were resettled shows that this is not necessarily the case. Having observed resettlement experiences in the Italian region of Molise and the French region of Alsace, we discovered that, wherever migrants are hosted within the confines of a village, villagers get frequent opportunities to meet them, learn to communicate with them, and spontaneously offer help, especially to children, women, and whole families. The lack of a common language does not prevent day-to-day iwnteractions or development of interpersonal relations. Children go to school and are keen to learn the host society’s language; adult migrants receiving help want to reciprocate by working for free, thus allowing them to quickly learn European ways and skills. If most asylum seekers eventually leave for larger cities, the months spent in a village prove to be a useful step preparing them for further resettlement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".