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Record W3196884827 · doi:10.4000/remi.15022

L'accueil hors des grandes villes

2020· paratext· fr· W3196884827 on OpenAlexaboutno aff

Bibliographic record

VenueRevue européenne de migrations internationales · 2020
Typeparatext
Languagefr
FieldSocial Sciences
TopicMulticulturalism, Politics, Migration, Gender
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Ce dossier de la Revue Européenne des Migrations Internationales est consacré à l’accueil des exilé·e·s hors des grandes villes et métropoles, dans les « petits milieux de l’immigration ». Ces « petits milieux » sont des lieux dont la population générale est de petite taille, voire de très petite taille au regard des métropoles les plus proches. Les minorités ethniques et populations issues de l’immigration y sont peu nombreuses, tout comme les structures associatives d’accueil ainsi que les possibilités d’emploi. Ce dossier propose, à travers des recherches d’horizons disciplinaires variés — sociologie, science politique, anthropologie, et géographie —, des travaux empiriques décrivant les politiques de dispersion des exilé·e·s et leurs effets sur les nouveaux territoires d’accueil ainsi que pour les personnes exilées elles-mêmes. Ce prisme permet d’interroger les conditions politiques dans lesquelles se mettent en place ces dispositifs de dispersion en France, mais également en Italie, au Royaume-Uni et au Canada. Il permet aussi d’étudier les modes d’action publique notamment mis en œuvre par les élus locaux et les bailleurs sociaux afin de redynamiser ces territoires, et de mieux saisir les formes de travail social et de militantisme qui se côtoient pour accompagner les exilé·e·s. Ce prisme permet enfin de rendre compte des effets de la dispersion sur les parcours migratoires des exilé·e·s. Ainsi, ce dossier analyse comment les politiques de répartition articulent des processus de mise à l’abri et de contrôle toujours croissant avec des politiques de redynamisation de territoires confrontés à des enjeux démographiques et économiques majeurs. This special issue of the Revue Européenne des Migrations Internationales turns a critical eye to the reception of exiles outside cities and metropolis, in “small immigration localities”. These “small localities” are places where the general population is small, or very small compared to the nearest metropolises. Here, ethnic minorities and populations of immigrant origin are few, as are the requisite support services and employment opportunities. Featuring research from various disciplines — sociology, political science, anthropology, and geography — this issue offers empirical work analyzing national dispersal policies and the potential repercussions for exiles and their new host territories. This lens makes it possible to examine the political conditions under which these policies are implemented in France, as well as in Italy, in the United Kingdom and in Canada. It also provides an understanding of the initiatives taken up by local elected officials and providers of social housing working to revitalize the territories in question, and of the forms of social work and activism aimed at welcoming exiles. Finally, it allows for the effects of such dispersion on the migratory routes of exiles to be taken into account. This special issue thus offers an analysis of how these dispersal policies, aligned as they are with policies calculated to revitalize territories facing major demographic and economic challenges, articulate processes of sheltering and ever-increasing control of exiled populations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.056
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.009
Scholarly communication0.0100.004
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0170.002

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.

Opus teacher head0.180
GPT teacher head0.398
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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