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Pension reform in China

2008· article· en· W1982863360 on OpenAlexaff
Felix Salditt, Peter Whiteford, Willem Adema

Bibliographic record

VenueInternational Social Security Review · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSocial securityChinaScope (computer science)PensionBusinessPopulationPopulation ageingEconomic growthNational PensionOld Age SecurityFinanceActuarial scienceEconomicsPolitical scienceBirth rateMarket economySociology

Abstract

fetched live from OpenAlex

Abstract This article analyses China's progress in creating a national old‐age insurance system, providing a detailed description of the system and an assessment of the degree to which it has so far realised its primary goal of social security for more people. Since 1997, there have been many reforms, but despite progress, the scope of the system is limited, with the coverage rate among urban employees being below 50 per cent. The rural population largely remains outside the system, and it seems likely that the majority of the population will be dependent on family support for many years to come. There is a “demographic window” until around 2015 to address these shortcomings. Extending coverage through improved compliance by employees and companies as well as the continuing financial commitment towards the National Social Security Fund are crucial to create the financial and institutional basis that can cushion the effects of a much older population in the years ahead.

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)0.002
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.122
Threshold uncertainty score0.242

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.021
GPT teacher head0.329
Teacher spread0.307 · 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

Citations57
Published2008
Admission routes1
Has abstractyes

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