Migration policies and practices at job market participation: perspectives of highly educated Turks in the US, Canada and Europe
Notice bibliographique
Résumé
Purpose This study aims to clarify the labor market participation of highly educated Turks who moved or were exiled to the Western countries after the July 15th, 2016 Coup attempt in Turkey. These recent Turkish flows create a compelling case for researching higher education connections and the administration of justice in migration policies/practices related to highly educated people's job market participation. This study aims to expand the discussion on migration policies, practices, job market participation, how highly skilled migrants perceive them in various contexts and understand the complexity of highly educated migrants' incorporation into destination countries and their perspectives and lived experiences with policy practice. Design/methodology/approach The primary source of the data is the semi-structured 30 interviews with the highly educated Turkish immigrants and refugees in Western countries, which enables comparative data from individuals of the same origin. The qualitative data have been transcribed, coded and analyzed according to the grounded-theory design from this vulnerable community. The high education was determined as graduation from 4-years colleges, which was recognized by destination countries. Our methodological tools were driven by the obstacles to collect data from politically sensitive, forced, or exiled migrants. Findings First, this article challenges the assumption that incorporating job market participation is a smooth process for highly educated migrants who moved to Western countries. Second, highly educated immigrants tried to reach their previous statuses and life standards as fast as possible by working hard, making sacrifices and developing innovative strategies. The immigrants in Europe have faced greater obstacles with policies while participating in the job market. Third, the importance of networking and the active usage of social media platforms to communicate with other immigrants in similar situations facilitated the job market participation and job preferences of highly educated migrants. Fourth, while fast job market participation experiences of immigrants in Northern America were increasing their positive feelings regarding belonging, people who have similar skillsets in Europe experienced more problems in this process and felt alone. Research limitations/implications The research results may lack generalizability due to the selected research approach. Further studies are encouraged to reach more population for each country to compare them. Practical implications Consequently, higher education may be a more vital decision point in migration policies and practices. This study contributes to a better understanding of these factors by showing the perspectives and experiences of highly educated migrants comparatively. Thus, it broadens the discussion about migration policies and job market participation of highly educated migrants. Social implications Building on this work, the authors suggest more studies on the temporary deskilling of highly educated migrants until they reach re-credentialing/education or training to gain their former status. Originality/value First, while most studies on immigrants' labor market participation and highly educated immigrants focus on voluntary migrants, this study examines underrepresented groups of involuntary migrants, namely forced migrants and exiled people, by focusing on non-Western Muslim highly educated Turks. Second, the trouble in the Middle East continues and regimes change softly or harshly. There is a growing tendency to examine these topics from the immigrants' perspective, especially from these war-torn areas. This article adds to this discussion by stating that rather than forced migration due to armed conflict, the immigrants from Turkey – the non-Arab Muslim state of the Middle East – are related to political conditions. Lastly, drawing on the relationship between social change in the origin country and migration and addressing the lack of reliable and comparative data, this study focuses on same origin immigrants comparatively in eight different countries.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».