Social, Economic and Demographic Consequences of Migration on Kerala
Notice bibliographique
Résumé
Migration has been the single most dynamic factor in the otherwise dreary development scenario of Kerala during the last quarter of the last century. It has contributed more to poverty alleviation and reduction in unemployment in Kerala than any other factor. As a result of migration, the proportion of the population below the poverty line has declined by 12 per cent. The number of unemployed persons – estimated to be only about 13 lakhs in 1998 compared with 37 lakhs reported by the Kerala Employment Exchanges – has declined by over 30 per cent. Migration has caused nearly a million married women in Kerala to live away from their husbands. Most of these so‐called “Gulf wives” experienced extreme loneliness to begin with, and were burdened with added family responsibilities to which they had not been accustomed when their husbands were with them. But over a period, and with a helping hand from abroad over the ISD, most came out of their early gloom. Their gain in autonomy, status, management skills and experience in dealing with the world outside their homes were developed the hard way and would remain with them for the rest of their lives for the benefit of their families and society. In the long run, the transformation of these million women will have contributed more to the development of Kerala society than all the temporary euphoria created by remittances and modern gadgetry. Kerala is dependent on migration for employment, subsistence, housing, household amenities, institution building, and many other developmental activities. The danger is that migration could cease, as shown by the Kuwait war of 1993, and repercussions could be disastrous for the State. Understanding migration trends and instituting policies to maintain the flow of migration is more important today than at any time in the past. Kerala workers seem to be losing out in international competition for jobs in the Gulf market. Corrective policies are needed urgently to raise their competitive edge over workers in competing countries in South and South‐East Asia. Like any other industry, migration from Kerala needs periodic technological upgrading of workers. Otherwise, there is a danger that the State might lose the Gulf market permanently. The crux of the problem is Kerala workers' inability to compete with expatriates from other South and South‐East Asian countries. The solution lies in equipping workers with better general education and job training. This study suggests a twofold approach. In the short run, the need is to improve the job skills of prospective emigrant workers. This could be achieved through ad hoc training programmes focussed on the job market in Gulf countries. In the long run, the need is to restructure the educational system, taking into consideration the future demand of workers not only in Kerala but also in potential destination countries all over the world, including the US and other developed countries. Kerala emigrants need not always be construction workers in the Gulf countries; they could also be software engineers in developed 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 machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».