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Record W2100831250 · doi:10.1177/0097700403259131

Converting Land to Nonagricultural Use in China’s Coastal Provinces

2004· article· en· W2100831250 on OpenAlexaff
Samuel P. S. Ho, George C. S. Lin

Bibliographic record

VenueModern China · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsChinaUrbanizationGeographyIndustrialisationHuman settlementPaceLand useRural settlementRural areaAgricultural economicsEnvironmental protectionEconomic growthPolitical scienceEcologyEconomicsArchaeology

Abstract

fetched live from OpenAlex

Since the mid-1980s, the conversion of land to nonagricultural use in China has been arguably the most widespread in the country’s history, and in no region has the process been more intense than in China’s coastal provinces. Among the more important factors that have contributed to the conversion of land to nonagricultural use are rural-urban migration, rapid economic growth, and increased investments in roads. A study of Landsat photographs of one south Jiangsu region reveals that because rural settlements are scattered and use a large amount of land and because urbanization and industrialization have occurred in a decentralized fashion, the shift in land use has not been restricted to a few major cities but has been widely dispersed. The article concludes by arguing that while the conversion of land to nonagricultural use in the coastal provinces is bound to continue, its pace will be slower than in the recent past.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.510
Threshold uncertainty score0.986

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.194
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 source (direct Gemma or distilled Codex), 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

Citations115
Published2004
Admission routes1
Has abstractyes

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