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Record W2133609788 · doi:10.5430/ijba.v5n6p65

Research on Perfecting the Rural Social Endowment Insurance System in Yangtze River Delta

2014· article· en· W2133609788 on OpenAlexvenueno aff
Shufen Zhou, Lin Han, Hong Wang, Keying Bi

Bibliographic record

VenueInternational Journal of Business Administration · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Environment
Canadian institutionsnot available
Fundersnot available
KeywordsEndowment policyBusinessDeltaYangtze riverSubsidyGovernment (linguistics)EndowmentChinaRural areaFinanceEconomicsNatural resource economicsGeographyPolitical science

Abstract

fetched live from OpenAlex

Objectives: This paper analysis and summary rural endowment insurance and the typical model of the Yangtze River Delta region , from the cover, government subsidies and the level of treatment and other aspects rural pension insurance of the Yangtze River Delta region in China and the existing problems in the implementation. Methods: Construct the Yangtze River Delta new rural endowment insurance system based on urban-rural integration; calculate through the establishment of new agricultural insurance system balance model, analysis the balance of new plan.Results: The new rural endowment insurance system designed in this paper cover the allied farmers, land expropriated farmers and urban residents and other free occupation. It implements the work method of the government and the insured voluntary combination, and takes the mode of" individual account pension + basic pension +society as a whole".Discussion: Improve the funding model and fund management of the Yangtze River Delta new rural endowment insurance system. Straighten the management services of the Yangtze River Delta region rural endowment insurance system.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.060
GPT teacher head0.363
Teacher spread0.303 · 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 designNot applicable
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
Published2014
Admission routes1
Has abstractyes

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