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Record W2379478440

Polices,Laws and Regulations on Land Reclamation and the Implications:Comparing China with Other Countries

2009· article· en· W2379478440 on OpenAlexaboutno aff
Bian Zheng-fu

Bibliographic record

VenueZhongguo tudi kexue · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental and Agricultural Sciences
Canadian institutionsnot available
Fundersnot available
KeywordsLand reclamationChinaIncentiveBusinessLand administrationImperfectLawEnvironmental planningPublic administrationPolitical scienceEconomicsEnvironmental scienceGeographyMarket economy
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this paper is to bring forth the suggestion on revising and enhancing theLand Reclamation Ordinancein China through comparing the advanced experience in policies,laws and regulations on land reclamation in other countries with that of China.Methods of documentation and theoretical studies were employed.Results indicate that there are problems regarding currentLand Reclamation Regulationsand other relevant laws and regulations,such as the imperfect system,unclear duties and rights of land reclamation,and uncertain sources of funds for land reclamation.As a result,the knotty problems in relevant practices can't be solved under current law or policy settings,and the progress of current land reclamation is very slow.However,the great achievements made in land reclamation in some foreign countries just benefited from strict laws and regulations of land reclamation as well as perfect administration system.It is concluded that we should refer to the advanced experience on land reclamation in US,Germany,Canada and UK,and try to combine them with Chinese situation.To prefect the policies,laws and regulations on land reclamation in China can be attempted in the ways of perfecting law system,completing organizations structures,establishing standard system,stabilizing funding source for reclamation,setting up an incentive mechanism as well as enhancing propaganda and education,and so on.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.191
Teacher spread0.186 · 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

Citations3
Published2009
Admission routes1
Has abstractyes

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