MétaCan
Menu
Back to cohort
Record W3128108975 · doi:10.18778/1231-1952.27.2.10

Migrations of elderly people in the world and in Poland

2020· article· en· W3128108975 on OpenAlexaboutno aff
Sławomir Pytel, Wioletta Kamińska, Iwona Kiniorska, Patryk Brambert

Bibliographic record

VenueEuropean Spatial Research and Policy · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPaceGeographyMediterranean climateEconomic geographyPolitical scienceEconomyDevelopment economicsEconomic growthEconomicsArchaeology

Abstract

fetched live from OpenAlex

Migrations of seniors in the 21st century accurately reflect the socio-demographic changes in developed countries. Their intensity increases in various parts of the world. In Europe, pensioners from the north move to the region of the Mediterranean Sea. Seniors from the United States and Canada are attracted to the countries of Central and South America. The goal of this study is to identify the trends in foreign migrations of seniors in selected countries of the world, with special regard to the migration of Polish pensioners. The study shows that contemporary seniors can afford to purchase property abroad and the driving forces for the migration movement include: warm climate, beautiful landscape, and a healthier and slower pace of living at the final destination. However, when it comes to Polish pensioners, the main reason for their migrations is their attempt to improve their economic conditions.

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)0.000
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 score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.075
GPT teacher head0.393
Teacher spread0.318 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Spatial Research and PolicySame topicMigration, Aging, and Tourism StudiesFrench-language works237,207