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Record W1982483775 · doi:10.3828/tpr.79.2-3.7

Planning a mega-city's future: <i>An evaluation of Shanghai's municipal land-use plan</i>

2008· article· en· W1982483775 on OpenAlexaff
John Meligrana, Wenwei Ren, Zhiyao Zhang, Bruce C. Anderson

Bibliographic record

VenueTown Planning Review · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsQueen's University
FundersNational Development and Reform CommissionMinistry of Land and Resources of the People's Republic of China
KeywordsPlan (archaeology)Strengths and weaknessesRestructuringEnvironmental planningContext (archaeology)Land useChinaUrban planningLand-use planningRegional planningBusinessRegional sciencePolitical scienceGeographyCivil engineeringEngineering

Abstract

fetched live from OpenAlex

This article provides a detailed evaluation of Shanghai's municipal land-use plan (SMLUP). It fills a gap in the literature that has examined Shanghai's urban growth and economic restructuring, but has neglected to evaluate the city's municipal plan. This article sets the evaluation of the SMLUP within the administrative and institutional context of the preparation of comprehensive land-use plans in China as well as the extensive literature on Shanghai's urban spatial structure. It identifies a number of strengths and weaknesses of the SMLUP. Overall, the SMLUP was found to be a rather technical document that attempts to balance the supply of agricultural and development land. The discussion traces the weaknesses to poorly defined planning terms and concepts within the plan, as well as narrow regulations governing the creation of comprehensive land-use plans. Inconsistencies are also found between policies and objectives contained within the SMLUP and the Comprehensive Plan of Shanghai. The paper concludes that a more comprehensive and holistic plan evaluation framework is required if Chinese city-regions are to be governed by better land-use plans.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.140
GPT teacher head0.376
Teacher spread0.236 · 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 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

Citations8
Published2008
Admission routes1
Has abstractyes

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