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Enregistrement W3125001593

Socio-Economic and demographic consequences of migration in Kerala

2000· preprint· en· W3125001593 sur OpenAlex

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Notice bibliographique

RevueOpenDocs (Institute of Development Studies) · 2000
Typepreprint
Langueen
DomaineSocial Sciences
ThématiqueSocial and Economic Development in India
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPovertyUnemploymentPopulationQuarter (Canadian coin)LonelinessEconomic growthDevelopment economicsFeelingUrbanizationPolitical scienceGeographyDemographic economicsSocioeconomicsSociologyEconomicsDemographyPsychology
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Migration has been the single most dynamic factor in the otherwise
\ndreary development scenario of Kerala in the last quarter of the past
\ncentury. Migration has contributed more to poverty alleviation and
\nreduction in unemployment in Kerala than any other factor. As a result
\nof migration, the proportion of population below the poverty line has
\ndeclined by 12 per cent. The number of unemployed persons - estimated
\nto be only about 13 lakhs in 1998 as against 37 lakhs reported by the
\nEmployment Exchanges - has come down by more than 30 per cent.
\nMigration has caused nearly a million married women in Kerala
\nto live away from their husbands. Most of these women, the so-called
\n"Gulf wives" had experienced extreme loneliness to begin with; but they
\ngot increasingly burdened with added family responsibilities with the
\nhandling of which they had little acquaintance so long as their husbands
\nwere with them. But over a period of time, and with a helping hand from
\nabroad over the ISD, most of them came out of their feeling of
\ndesolateness. Their sense of autonomy, independent status, management
\nskills and experience in dealing with the world outside their homes - all
\ndeveloped the hard way - would remain with them for the rest of their
\nlives for the benefit of their families and the society at large. In the longrun,
\nthe transformation of these one million women would have
\ncontributed more to the development of Kerala society than all the
\ntemporary euphoria created by foreign remittances and the acquisition
\nof modern gadgetry.
\nKerala is becoming too much dependant on migration for
\nemployment, sustenance, housing, household amenities, institution
\nbuilding, and many other developmental activities. The inherent danger
\nof such dependence is that migration could stop abruptly as was shown by the Kuwait war experience of 1990 with disastrous repercussions for
\nthe state. Understanding migration trends and instituting policies to
\nmaintain the flow of migration at an even keel is more important today
\nthan at any time in the past. Kerala workers seem to be losing out in the
\ninternational competition for jobs in the Gulf market. Corrective policies
\nare urgently needed to raise their competitive edge over workers in the
\ncompeting countries in the South and the South East Asia. Like any
\nother industry, migration needs periodic technological up-gradation of
\nthe workers. Otherwise, there is the danger that Kerala might lose the
\nGulf market forever.
\nThe core of the problem is the Kerala worker's inability to compete
\nwith expatriates from other South and South Asian countries. The solution
\nnaturally lies in equipping our workers with better general education
\nand job training. This study suggests a two-fold approach - one with a
\nlong-term perspective and the other with a short-term perspective. In the
\nshort-run, the need is to improve the job skills of the prospective emigrant
\nworkers. This is better achieved through ad hoc training programmes
\nfocussed on the job market in the Gulf countries. In the long-run, the
\nneed is to restructure the whole educational system in the state taking
\ninto consideration the future demand for workers not only in Kerala but
\nalso in the potential destination countries all over the world, including
\nthe USA and other developed countries. Kerala emigrants need not always
\nbe construction workers in the Gulf countries; they could as well be
\nsoftware engineers in the developed countries.
\nJEL Classification : J16, J21, J23
\nKey words : Kerala, emigration, return migration, remittances, gender,
\ndemography, elderly

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.

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)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,459
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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 Opus0,056
Tête enseignante GPT0,327
Écart entre enseignants0,271 · 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