MétaCan
Menu
Back to cohort
Record W2993135632

A Study on Income-Distribution Regulatory Effect of Financial Support in Agriculture

2014· article· en· W2993135632 on OpenAlexvenueno aff
Hong-Yu Tian, Zhiyong Zhu

Bibliographic record

VenueCanadian social science · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureDistribution (mathematics)Scale (ratio)Comprehensive incomeEconomicsCapital (architecture)Agricultural economicsBusinessIncome distributionLabour economicsPublic economicsGross income
DOInot available

Abstract

fetched live from OpenAlex

This thesis carries out a quantitative inspection about marginal scale effect and structural effect that both size and structure of expenditures for financial support in agriculture have on income gap between urban and rural residents. According to analyses, it is found that both absolute amount and relative amount of size of financial support in agriculture have widening effect on income gap between urban and rural residents; agricultural infrastructure, three items of agricultural science and rural relief have gap-reducing effect; support for agricultural production and operating expenses of departments do not have significant effect, which may have widening effect in combination with reality factors; and unreasonable structure is a key factor leading to the situation that regulatory effect of financial support in agriculture on unfair income distribution is minor. Thus, to improve income-distribution regulatory effect of financial support in agriculture, it is essential to increase scale of expenditures, form a long-term mechanism, enhance degree of capital integration, optimize expenditure structure and perfect mechanisms of governmental performance examination.

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 score0.246
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.011
GPT teacher head0.285
Teacher spread0.273 · 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
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueCanadian social scienceSame topicIncome, Poverty, and InequalityFrench-language works237,207