Moving to a Non-Metropolitan Area: The Information Channels of International Migrants Going to the Department of Calvados
Bibliographic record
Abstract
In the context of the development of national dispersion policies in Europe, a growing body of literature has been focusing on small and mid-sized cities as points of arrival for migrants in these specific cases of forced mobility. However, little work has been done on the trajectories of migrants who voluntarily move to apparently unattractive territories. Based on the example of the department of Calvados in Normandy, and drawing on qualitative data, this paper analyses the information practices developed by international migrants that are in a situation of information precarity and who are faced with Europe’s externalized and internalized border control. By looking to the source and nature of elements that were the reason for their decision to move to Calvados, this article sheds light on the types of information and actors that shape migrants’ itineraries. Furthermore, this article discusses the dynamics of trust and distrust between migrants and their various interlocutors and questions the characteristics of those identified as relevant providers of information. This article highlights the impact of administrative procedures and reception arrangements on migrants’ trajectories, as well as the central, albeit ambivalent, role of transnational social capital in relation to finding the right information in a new territory.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".