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Enregistrement W4245652484 · doi:10.1111/imig.12203

Editorial

2015· editorial· es· W4245652484 sur OpenAlexaff
Howard Duncan

Notice bibliographique

RevueInternational Migration · 2015
Typeeditorial
Languees
DomainePsychology
ThématiqueMigration, Health and Trauma
Établissements canadiensCarleton University
Organismes subventionnairesnon disponible
Mots-clésRefugeeState (computer science)ImmigrationHappeningPolitical scienceData collectionPublic relationsLaw and economicsSociologyLawHistoryComputer scienceSocial science

Résumé

récupéré en direct d'OpenAlex

Data are the lifeblood of the social sciences, and it is our constant quest to acquire new data and to develop new ways to urge more and more knowledge from them. Migration data are particularly elusive given the lack of consensus regarding what to count or who ought to be considered a migrant, the fact that many migrants do not want to be counted and take precautions to prevent this from happening, and given that few governments record departures. One of the authors in this issue, Jonathan Moses, refers to the “appalling state of migration data” in the world today and notes the costs that this has for migration policy in societies both of origin and destination. Many countries that willingly accept if not actively recruit immigrants have good data on who has arrived and under what visa, but at the same time do not know who has left, let alone for what duration. Migration data are difficult and costly to obtain, and net migration data additionally so. This makes it all the more challenging for governments to manage migration even at the best of times. And these are not among the best of times for migration management. In this issue, we offer five articles on data collection and analysis, articles that confront the “appalling state of migration data” and lead the way to a better state of affairs through innovations in collection, estimations, analysis, and practical applications. We open with a remarkable innovation in ascertaining refugee populations, one that was borrowed from a way of estimating wildlife populations that was subsequently adapted for epidemiology and later by demographers and even by astronomers for estimating numbers of celestial bodies. The capture-recapture approach to estimating populations is applied to refugees in smaller geographical locales by Gold et al, and they demonstrate how this method can be used by local authorities in allocating resources for services to refugees. Makaryan, noting the many alternative methods for estimating migrant populations, considers the special problems in so doing in developing countries, specifically 15 states of the former Soviet Union. She notes the variety of definition of ‘migrant’ used and the attendant ambiguities that this lends the data, the failure of censuses to capture temporary migrants, and the provisional value of household surveys in measuring migration. Moses has responded to the state of migration data by launching EMIG 1.2: A global time series of annual emigration flows, an open-source database that is in its early stages of development but yet already offers significant potential to enhance our understanding of emigration should the global migration community participate in further developing the database. Analyses of the data to date confirm that not only are migration rates lower now than they were early in the twentieth century, they have been falling since 1994, something that will take many of us by surprise. Bailey and Lau turn our attention to Hong Kong which has undergone a major shift in migration since the re-unification with China which has led to a highly dynamic two-way flow of workers, students, and settlers. They propose a new method for categorizing and measuring flows as well as new institutional mechanisms to co-ordinate data collection with policy making. In an article that bridges our two themes for this issue, Mendoza examines emigration patterns from a municipality in Mexico City, seeking insights by comparing households with and without emigrants. Her logistic regression models reveal the signal importance of social networks in motivating departures. Our second theme for this issue is the motivations for emigration. Seven articles contribute important nuances to our understanding of what underlies people's decisions to leave their homelands, nuances that we hope will further enliven both the theoretical debates about what lies behind decisions to migrate and enhance the level of understanding among policy organizations. In looking at contemporary Kosovo, a fledgling country suffering from very high levels of departures, Ivlevs and King note the lack of confidence in the future of the country and its economy amongst especially those Albanian Kosovars with higher levels of education. Emigration aspirations have returned to levels not seen since before independence, a trend that may itself fuel an even greater demand to leave this struggling country. Cohen, Duberley, and Ravishankar consider how Indian scientists employ international mobility as a career enhancer, a strategy that allows them gain valuable international experience while preserving their cultural ties to India as well as their ability to return to India in a more advanced position. Weeks and Weeks examine the role of transnationalism in contemporary emigration from Latin America to the United States, going beyond the lure of better-paying jobs that support remittances to the supportive roles increasingly played by their homeland governments in protecting their rights while abroad and encouraging their return. Staying within Latin America, Silva and Massey detail the role of violence in motivating migration out of Columbia. Not as straightforward as one might be tempted to imagine, violence tends to lead to emigration predominantly for those with higher levels of education and stronger social networks abroad. While violence can bring about a decision to leave, it is social capital networks that determine destinations. Bylander brings us to Cambodia to explore how actual and anticipated environmental distress motivates decisions to emigrate. Gerver brings the perspective of moral philosophy to voluntary repatriation, offering a careful normative analysis of the tension between facilitating repatriation to restore rights and ensuring that the repatriation is in fact voluntary. Finally, we return to a post-Soviet state to look at mass emigration from Lithuania, the first Soviet state to declare independence in 1990. Klusener et al use census and registration data to document that it is characteristics such as employment status, education, and prior migration experience that influence decisions to leave.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,011
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,007

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,019
Tête enseignante GPT0,359
Écart entre enseignants0,340 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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