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Record W3031237126 · doi:10.33212/ppc.v3n1.2020.66

China’s demographic prospects to 2040 and their implications: an overview

2020· article· en· W3031237126 on OpenAlexaboutno aff
Nicholas Eberstadt

Bibliographic record

VenuePsychoanalysis and Psychotherapy in China · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsnot available
Fundersnot available
KeywordsChinaPopulationUrbanizationDemographyQuarter (Canadian coin)Population ageingPacePopulation declineGeographyDemographic economicsMass migrationOne-child policyPopulation growthDevelopment economicsSocioeconomicsEconomic growthEconomicsSociologyFamily planningImmigration

Abstract

fetched live from OpenAlex

China’s population prospects over the decades ahead are largely shaped by pro-longed sub-replacement childbearing, likely to have been in effect for half a century by 2040. China’s population is on track to peak in the coming decade and to decline at an accelerating pace thereafter. Between 2015 and 2040, China’s population aged 50 and older is on course to increase by roughly one-quarter of a billion people; the under-50 population is set to decline by a roughly comparable magnitude. China is set to experience an extraordinarily rapid surge of population aging, with especially explosive population growth for the 65-plus group, even as its working-age population (conventionally defined as the age 15–64 group) progressively shrinks. Additionally, a number of demographic changes underway now constitute “wild cards” for China’s future: including (1) the impending “marriage squeeze” due to abnormal sex ratios at birth from the one-child policy era; (2) the problem of mass urbanisation under a system that consigns migrants in urban areas to an officially inferior status; and (3) the revolutionary changes in the Chinese family structure, which portend a dramatic departure from previous arrangements on which Chinese society and economy depended.

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 score0.652
Threshold uncertainty score0.829

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.067
GPT teacher head0.361
Teacher spread0.295 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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