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Record W4213295302 · doi:10.1111/padr.12469

Six Ways Population Change Will Affect the Global Economy

2022· article· en· W4213295302 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePopulation and Development Review · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsnot available
FundersEconomic and Social Research CouncilWilliam and Flora Hewlett FoundationEuropean CommissionInternational Development Research CentreUnited Nations Population FundNational Institute on AgingWorld Bank Group
KeywordsPopulationEconomicsStandard of livingPopulation ageingProductivityPopulation growthDistribution (mathematics)Demographic economicsDemographic changeAffect (linguistics)DebtDevelopment economicsEconomic growthDemographyMarket economyMacroeconomics

Abstract

fetched live from OpenAlex

Abstract New estimates of economic flows by age combined with population projections show that in the coming decades (1) global GDP growth could be slower by about 1 percentage point per year, declining more sharply than population growth; (2) GDP will shift toward sub‐Saharan Africa more than population trends suggest; (3) living standards of working‐age adults may be squeezed by high spending on children and seniors; (4) changing population age distribution will raise living standards in many lower‐income nations; (5) changing economic life cycles will amplify the economic effects of population aging in many higher income economies; and (6) population aging will likely push public debt, private assets, and perhaps productivity higher. Population change will have profound implications for national, regional, and global economies.

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.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.566
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.089
GPT teacher head0.256
Teacher spread0.168 · 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