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Linking institutional context to the community and career embeddedness of skilled migrants: The role of destination and origin country identifications

2024· article· en· W7029868667 sur OpenAlexfundno aff

Notice bibliographique

RevueDeposito Adademico Digital Universidad De Navarra (University of Navarra) · 2024
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueMigration, Ethnicity, and Economy
Établissements canadiensnon disponible
Organismes subventionnairesAgencia Estatal de InvestigaciónEuropean Regional Development FundMinisterio de Ciencia e InnovaciónCanada Research ChairsEuropean Commission
Mots-clésEmbeddednessDisadvantageMultinational corporationNature versus nurtureCountry of originLeverage (statistics)Context (archaeology)Identification (biology)Affect (linguistics)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Abstract \n \nMigration is one of the most pressing global issues of our time. However, relatively little is known about the factors and mechanisms that govern the post-migration experiences of skilled migrants. We adopt an acculturation- and social identity-based approach to examine how differences between institutional characteristics in the destination and origin country, as well as migrants’ experiences with formal and informal institutions shape their identification with the destination and origin country and contribute to their community and career embeddedness. Our study of 1709 highly skilled migrants from 48 origin countries in 12 destination countries reveals that the institutional environment migrants encounter provides both sources of opportunity (potential for human development and value-congruent societal practices) and sources of disadvantage (experienced ethnocentrism and downgrading). These contrasting dynamics affect migrants’ destination-country identification, their origin-country identification and, ultimately, their embeddedness in the destination country. Our results have important implications for multinational enterprises and policy makers that can contribute to enhancing skilled migrants’ community and career embeddedness. For example, these actors may nurture a work environment and provide supportive policies that buffer against the institutional sources of disadvantage we identified in this study, while helping migrants to leverage the opportunities available in the destination country. \n \nPlain language summary \n \nMigration is a pressing worldwide issue, yet there is limited understanding of the factors that influence the experiences of skilled migrants (individuals who move from one country to another, often for work or education) after they relocate. This study investigates how differences between institutional characteristics (the characteristics of a country's social, economic, and political systems) in the destination (the country to which a person migrates) and origin country (the country from which a person migrates), as well as migrants' experiences with these institutions, influence their identification (feeling of belonging or association) with the destination and origin country and contribute to their community and career embeddedness (the degree to which they feel integrated and established in their community and profession). The study involved 1709 highly skilled migrants from 48 origin countries in 12 destination countries. The researchers used datasets from the UNDP’s Human Development Index (a summary measure of average achievement in key dimensions of human development) and the GLOBE database (a research program studying cross-cultural management) to measure institutional and societal influences. They also developed new measures to capture migrants’ experiences and their identification with the destination and origin countries. The results revealed that migrants who moved to a country that offered better opportunities for human development and societal practices (the way a society behaves, operates, and functions) that were more aligned with their values had higher destination-country identification and were more embedded in their communities and careers. However, migrants who experienced occupational downgrading (reduction in job status or pay) or ethnocentrism (belief in the superiority of one's own ethnic group) in the destination country had lower destination-country identification and were less embedded. The researchers concluded that while destination-country identification is crucial for achieving high levels of community and career embeddedness, origin-country identification also matters, but not in the way predicted by acculturation research (study of how individuals adopt the cultural traits of another group). They suggested that future research should investigate the experiences of migrants moving from an economically developed to a less developed country and test the proposed relationships between opportunities for development and country identifications. This study has significant implications for migration policies and practices. It suggests that governments and organizations need to consider not only the economic opportunities they offer to migrants but also the societal practices and experiences that shape migrants' identification with the destination country. This could help to enhance skilled migrants' community and career embeddedness and increase their retention in the destination country.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,012
Score d'incertitude au seuil0,034

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,002
Communication savante0,0030,001
Science ouverte0,0010,004
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0100,000

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 Opus0,017
Tête enseignante GPT0,249
Écart entre enseignants0,233 · 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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

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
Publié2024
Routes d'admission1
Résumé présentoui

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