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Record W4408030379 · doi:10.4000/13eks

Population and mobility in the Portuguese islands: trends from 2001 to 2021

2024· article· en· W4408030379 on OpenAlexaff
María Cristina Sousa Gomes, Dulce Pimentel, Isabel Tiago de Oliveira, Patrícia Ferraz de Matos

Bibliographic record

VenueEspace populations sociétés · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsPortugueseGeographyEconomic geographyPopulationDemographySociologyLinguistics

Abstract

fetched live from OpenAlex

The aim of this paper is to analyse the demographic and migratory dynamics in the Portuguese island regions of Madeira and the Azores over the last two decades. The article begins with a brief overview of the historical and geographical context, which is essential for understanding the factors that shape population evolution and are reflected in the current situation. Beyond physical constraints such as insularity, it is important to highlight the significant historical influence of emigration in these regions and its relationship with the development of the islands over time.There have been significant changes in population growth patterns from 2001 to 2021, with a decrease in natural growth and an increased importance of migration for demographic evolution. In particular, emigration emerges as a response to economic vulnerability, playing a crucial role in moments of crisis, such as the recession of 2011-2013.Traditionally areas of emigration, the islands have experienced an increase in the proportion of foreign population, a phenomenon that reflects changes in migratory trends, particularly in Madeira, which receives a significant proportion of people from abroad.From a demographic point of view, the islands face the challenge of an ageing population, although they still have a younger demographic structure than the mainland. However, despite these changes, vulnerability persists, as reflected in indicators such as life expectancy.

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)0.001
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 score0.738
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

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

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 head0.069
GPT teacher head0.416
Teacher spread0.347 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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