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Future Changes in Age Structure and Different Migration Scenarios

2019· article· en· W2986831404 on OpenAlexaboutno aff
Víctor M. García-Guerrero, Claudia Masferrer, Silvia E. Giorguli Saucedo

Bibliographic record

VenueRevista Latinoamericana de Población · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsLife expectancyDestinationsGeographyAge structurePopulationConvergence (economics)Human migrationEconomic geographyDemographic changeInternal migrationFertilityPopulation ageingDeveloped countryDemographic economicsDemographyEconomic growthEconomicsSociologyTourism

Abstract

fetched live from OpenAlex

We analyze migration and demographic changes among the six countries of North America (NA) and the Northern Triangle of Central America (NTCA, i.e. Guatemala, Honduras and El Salvador). Together, they comprise a long-standing South-North migration stream, with the United States (US) and Canada being the main destinations for Mexico and the NTCA. Studies that analyze the demographic effects of international migration in origin and destination countries have been limited. In order to fill this gap and explain the implications of recent changes in migration trends and demographic dynamics of the six countries, we study the interrelationship between future changes in the age structure associated with different migration scenarios. We use data from the United Nations World Population Prospects 2017 to compare the main demographic indexes and age structure indicators under two prospective scenarios: with and without migration. Current and projected population dynamics suggest convergence in fertility below replacement levels, higher life expectancy, and an overall aging process in the NA-NTCA region. Future migration may slow down the aging process in Canada and the US, have a small effect in Mexico, and speed it up in El Salvador. Taking both the size of the populations and the decrease in young age groups for the main sending countries we have studied, it is unlikely that international migration to the US from Mexico and the NTCA will reach the historic peak observed during the first decade of the 21st century.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.259
Teacher spread0.252 · 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 designSimulation or modeling
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

Citations4
Published2019
Admission routes1
Has abstractyes

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