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Record W2949261915 · doi:10.15273/ijge.2019.01.002

Perception of and Preference for Rural Built Environment of Rural Residents: Evidence from Sichuan

2019· article· en· W2949261915 on OpenAlexvenueno aff
Yuting Zhang, Yibin Ao, Kun Huang, Yan Wang, Yunfeng Chen, Xun Zhou

Bibliographic record

VenueInternational Journal of Georesources and Environment · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Environment
Canadian institutionsnot available
Fundersnot available
KeywordsPreferencePerceptionUrbanizationRural areaGeographySocioeconomicsBuilt environmentPsychologyEconomic growthPolitical scienceSociologyCivil engineeringEngineeringEconomics

Abstract

fetched live from OpenAlex

Rural areas are undergoing tremendous changes due to rapid urbanization and new construction. However, few studies have investigated the perspectives of local residents on the changing landscape. This study investigates the aforementioned phenomenon via field surveys conducted in seven villages and townships in Sichuan Province. Factor analysis was performed on 352 valid questionnaires to analyze the perceptions of and preferences for the built environment of rural residents. Each analysis extracted 5 factors from 20 variables. The comparative analysis identified three common factors, namely, convenient transportation, public environment and roads, which influenced the perceptions on and preferences for the rural built environment. However, the importance of each factor differed in terms of perception and preference. Results of the analysis and comparison highlight areas that can be improved and promoted in new rural construction. Suggestions for development and construction are provided to promote the progress of new countryside regions.

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 score0.126
Threshold uncertainty score0.441

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 head0.028
GPT teacher head0.266
Teacher spread0.238 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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