Rural-Urban Dynamics in the International Migration of Students from Kerala, India
Notice bibliographique
Résumé
Abstract The migration of youth from Kerala seeking higher education abroad has been rising significantly in recent years. This study explored the dynamics of international student migration from Kerala, focusing on rural and urban variations on push and pull factors, significant influences, and the challenges confronted by migrating students and their families in both host and home countries. The study was conducted using cross-sectional data gathered from 200 students from Kerala who are currently studying abroad. The results indicated that urban students were primarily pulled by the better employment and income prospects abroad, superior quality of overseas education, exposure to international culture, favourable policies in destination countries for international students, and ease of securing permanent residency. In contrast, rural students were more strongly pushed by the high levels of educational unemployment in Kerala, inadequate higher education facilities, and peer pressure. The most pressing issues encountered by overseas students in their host countries included the high cost of living, delay in getting permanent residency, low-paid part-time work, financial hardships, academic challenges, discrimination and racism, and language barriers. However, financial hardships, low-paid part-time work, academic challenges, discrimination and racism, and language barriers were more severely felt by rural students. Furthermore, the challenges these students face have been further worsened by recent policy changes enacted by the governments of Canada and the UK regarding international students. The families of overseas students in Kerala experienced serious issues such as huge debt liability, financial strain, loan default, increased household responsibilities, depression, and anxiety. These issues were more acute for families of rural students, except for increased household responsibilities. The insights from this study, along with proposed recommendations, could guide policymakers and educational institutions in both host and home countries in formulating specific interventions to address the issues confronted by the overseas students. Keywords: International Student Migration, Overseas Education, Push and Pull Factors, Rural-Urban Differences, Host Country Challenges, Issues of Families of Overseas Students.
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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 | 0,002 | 0,000 |
| 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 | 0,001 |
| Science ouverte | 0,001 | 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 ».