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Record W1900074679 · doi:10.25336/p6jw2p

Globalization, Migration and Development

2002· article· en· W1900074679 on OpenAlexaffvenue
John Samuel, Susan George

Bibliographic record

VenueCanadian Studies in Population · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsCarleton University
Fundersnot available
KeywordsPopulationHuman migrationUrbanizationDevelopment economicsInvestment (military)ImmigrationEmigrationGlobalizationEconomicsDilemmaEconomic growthPopulation growthFreedom of movementHuman capitalPoliticsPolitical scienceMarket economy

Abstract

fetched live from OpenAlex

Migration may become the most important branch of demography in the early decades of the new millennium in a rapidly globalizing world. This paper discusses the causes, costs and benefits of international migration to countries of the South and North, and key issues of common concern. International migration is as old as national boundaries, though its nature, volume, direction, causes and consequences have changed. The causes of migration are rooted in the rate of population growth and the proportion of youth in the population, their education and training, employment opportunities, income differentials in society, communication and transportation facilities, political freedom and human rights and level of urbanization. Migration benefits the South through remittances of migrants, improves the economic welfare of the population (particularly women) of South countries generally, increases investment, and leads to structural changes in the economy. However, emigration from the South has costs too, be they social or caused by factors such as brain drain. The North also benefits by migration through enhancement of economic growth, development of natural resources, improved employment prospects, social development and through exposure to immigrants’ new cultures and lifestyles. Migration also has costs to the North such as of immigrant integration, a certain amount of destabilization of the economy, illegal immigration, and social problems of discrimination and exploitation. Issues common to both North and South include impact on private investment, trade, international cooperation, and sustainable development. Both North and South face a dilemma in seeking an appropriate balance between importing South’s labour or its products and exporting capital and technology from the North.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.875
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.054
GPT teacher head0.317
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations17
Published2002
Admission routes2
Has abstractyes

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