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Record W2557285998 · doi:10.2495/sdp-v12-n5-922-932

Measurement of system coordination degree of china national sustainable communities

2016· article· en· W2557285998 on OpenAlexvenueno aff
Jingxia Wu, Xiao-Ming Wang, Xu Wang, Wei Peng

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsnot available
Fundersnot available
KeywordsDegree (music)ChinaSustainable developmentEnvironmental resource managementBusinessEnvironmental planningEnvironmental economicsEnvironmental scienceGeographyPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Local governments in China have been presented with an opportunity to become more sustainable through the program of China National Sustainable Communities (CNSCs).This program is aimed at guiding CNSCs toward sustainable development among resources, economy, environment and society.This article is focused on coordinated development patterns of CNSCs, in which the integrated coordination of CNSCs was analyzed.The entire process of the coordinated development of CNSCs being taken as object of study, the concept of coordination degree for CNSCs was proposed and a coordination degree evaluation system framework for CNSCs was built, which was divided into four subsystems, namely resource, economic, environment and social subsystems.Furthermore, a coordination degree evaluation indicator system for CNSCs was set up and the index weight was calculated based on component importance with the method of Principal Components Analysis used for data analysis.Finally, an evaluation model for coordination degrees of CNSCs was established.Besides, a hierarchy for coordination degrees to evaluate sustainable development levels was also set up.For application of the proposed model and the hierarchy for coordination degrees of CNSCs, two national sustainable communities, Chengmai county and Baisha county in Hainan Province, were analyzed for case study.

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)0.003
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.041
GPT teacher head0.283
Teacher spread0.242 · 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 designQualitative
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

Citations12
Published2016
Admission routes1
Has abstractyes

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