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Record W1962802506

Study on Dual-Track System of Chinese Land Ownership

2015· article· en· W1962802506 on OpenAlexvenueno aff
Zhengquan Liu, Meirong Liang

Bibliographic record

VenueCross-cultural communication · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLand Rights and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsChinaTrack (disk drive)BusinessCommon ownershipState (computer science)Dual (grammatical number)Government (linguistics)Land tenureUnitary stateLand lawUrban villageState ownershipEconomic growthGeographyAgricultureLawPolitical scienceEconomicsCivil engineeringEngineeringComputer science
DOInot available

Abstract

fetched live from OpenAlex

In China, there are two tracks in the system of land ownership which are respectfully adaptable for urban citizens and for rural farmers. The ownership of rural land belongs to the village whereas the ownership of urban land belongs to the state, the government, in a sense. Therefore, based on the double-track system the land in urban can be sold to anyone for use but the land in the village can only be sold to the villagers, that means, the identities of buyers must be restrained, which is absolutely unfair and unjust to Chinese people. Therefore we need to change the double-track system into unitary-track system, that means every citizen or farmer, whatever, so long as a Chinese would have the right to buy the land wherever is located in the rural area or in the urban area. Everyone in China has the right to buy or to sell the land you have occupied lawfully. Cancellation of the special track to farmer’s land, making all lands belong to the state, controlled and supervised by state special departments, is not just imperative, but also feasible.

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)0.002
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 score0.099
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.057
GPT teacher head0.318
Teacher spread0.261 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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