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Record W3187911928 · doi:10.15421/112133

International youth migration: features, tendencies, regulation prospects

2021· article· en· W3187911928 on OpenAlexaboutno aff
Sergii Sardak, Kateryna Shymanska, Alla Girman, Оleksandr Krupskyi

Bibliographic record

VenueJournal of Geology Geography and Geoecology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationEmigrationPolitical scienceAttractivenessGeographyDemographic economicsChinaHuman migrationDevelopment economicsEconomic growthEconomic geographyDemographyEconomicsSociology

Abstract

fetched live from OpenAlex

The article examines the global and regional issues of international youth migration. The obtained results are most interesting for those regions where the population is shrinking and aging with a rising need to involve youth for educational services and local labor markets, or vice versa, for those losing youth due to their emigration. It is emphasized that youth create an economically active social group, which volume and quality significantly affect the country’s development. During the global migration trends identification, the authors identified the international youth migration flows’ differences and features. The paper notes that the global trends in the international youth migration development include: increase in volume and percentage of youth in the overall number of migrants and the local population; growth of youth migrants in more developed regions and high-income countries; the dominance of migratory centers for youth in Oceania, North America, and Europe; formation of powerful centers of migration of intellectual young labor resources in the UAE, Canada, the USA, Australia, and New Zealand. The available formational policy in youth migration regulation, on the example of India, China, Taiwan, Japan, USA, and Western Europe, is studied. The impact of the COVID-19 pandemic on international migration flows is considered, including changes in international migration in 2020. Based on the UN data analysis on age groups of migrants within geographical regions, it was determined that the prerequisite for such a structure of migration centers is a high level of migratory attractiveness. Such migration-center structure is also explained by the significant level of cross-regional migration, as in the localized regions, their factors of «attraction-repulsion» are formed. It is stressed out that increas- ing military and political instability has led to the uphill of forced youth migrants. The paper proposes the flow optimization directions of international youth migration by formulating the link between migration policy and elements of other integration policies on migrant youth (employment policy, social, educational, information and security policies).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.011
GPT teacher head0.255
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2021
Admission routes1
Has abstractyes

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