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Record W4360612650 · doi:10.1553/p-g5fe-hafz

How much would reduced emigration mitigate ageing in Norway?

2023· article· en· W4360612650 on OpenAlexaboutno aff
Marianne Tønnessen, Astri Syse

Bibliographic record

VenueVienna Yearbook of Population Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInsurance, Mortality, Demography, Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsEmigrationAgeingFertilityPopulation ageingImmigrationPopulationDependency ratioDemographyQuarter (Canadian coin)Demographic economicsGeographyEconomicsMedicineSociology

Abstract

fetched live from OpenAlex

Population ageing is a topic of great concern in many countries. To counteract the negative effects of ageing, increased fertility or immigration are often proposed as demographic remedies. Changed emigration is, however, rarely mentioned. We explore whether reduced emigration could mitigate ageing in a country like Norway. Using cohort-component methods, we create hypothetical future demographic scenarios with lower emigration rates, and we present (prospective) old-age dependency ratios, population growth and shares of immigrants. We also estimate howmuch fertility and immigrationwould have to change to yield the same effects. In different scenarios, emigration is reduced for the total population and for subgroups, while also taking into account that reduced emigration of natives will entail reduced return migration. Our results show that even a dramatic 50% decrease in annual emigration would mitigate ageing only slightly, by lowering the old-age dependency ratio in 2060 from 0.54 to 0.52. This corresponds to the anti-ageing effect of 15% higher fertility, or one-quarter extra child per woman.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.171
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.124
GPT teacher head0.429
Teacher spread0.305 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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