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Enregistrement W3125001593

Socio-Economic and demographic consequences of migration in Kerala

2000· preprint· en· W3125001593 sur OpenAlexaboutno aff
K. C. Zachariah, Emil Mathew, S. Irudaya Rajan

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 dreary development scenario of Kerala in the last quarter of the past century. Migration has contributed more to poverty alleviation and reduction in unemployment in Kerala than any other factor. As a result of migration, the proportion of 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 as against 37 lakhs reported by the Employment Exchanges - has come down by more than 30 per cent. Migration has caused nearly a million married women in Kerala to live away from their husbands. Most of these women, the so-called "Gulf wives" had experienced extreme loneliness to begin with; but they got increasingly burdened with added family responsibilities with the handling of which they had little acquaintance so long as their husbands were with them. But over a period of time, and with a helping hand from abroad over the ISD, most of them came out of their feeling of desolateness. Their sense of autonomy, independent status, management skills and experience in dealing with the world outside their homes - all developed the hard way - would remain with them for the rest of their lives for the benefit of their families and the society at large. In the longrun, the transformation of these one million women would have contributed more to the development of Kerala society than all the temporary euphoria created by foreign remittances and the acquisition of modern gadgetry. Kerala is becoming too much dependant on migration for employment, sustenance, housing, household amenities, institution building, and many other developmental activities. The inherent danger of such dependence is that migration could stop abruptly as was shown by the Kuwait war experience of 1990 with disastrous repercussions for the state. Understanding migration trends and instituting policies to maintain the flow of migration at an even keel is more important today than at any time in the past. Kerala workers seem to be losing out in the international competition for jobs in the Gulf market. Corrective policies are urgently needed to raise their competitive edge over workers in the competing countries in the South and the South East Asia. Like any other industry, migration needs periodic technological up-gradation of the workers. Otherwise, there is the danger that Kerala might lose the Gulf market forever. The core of the problem is the Kerala worker's inability to compete with expatriates from other South and South Asian countries. The solution naturally lies in equipping our workers with better general education and job training. This study suggests a two-fold approach - one with a long-term perspective and the other with a short-term perspective. In the short-run, the need is to improve the job skills of the prospective emigrant workers. This is better achieved through ad hoc training programmes focussed on the job market in the Gulf countries. In the long-run, the need is to restructure the whole educational system in the state taking into consideration the future demand for workers not only in Kerala but also in the potential destination countries all over the world, including the USA and other developed countries. Kerala emigrants need not always be construction workers in the Gulf countries; they could as well be software engineers in the developed countries. JEL Classification : J16, J21, J23 Key words : Kerala, emigration, return migration, remittances, gender, demography, 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.

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)0,000
score de la tête « metaresearch » (Gemma)0,001
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 enseignants0,090
Score d'incertitude au seuil0,179

Scores du classifieur distillé par catégorie (deux têtes)

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

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.

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

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

Citations17
Publié2000
Routes d'admission1
Résumé présentoui

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