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Record W2944499528 · doi:10.3390/su11092716

Telecoupled Sustainable Livelihoods in an Era of Rural–Urban Dynamics: The Case of China

2019· article· en· W2944499528 on OpenAlexaff
Wenjia Peng, Brian E. Robinson, Hua Zheng, Cong Li, Fengchun Wang, Ruonan Li

Bibliographic record

VenueSustainability · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsMcGill University
Fundersnot available
KeywordsLivelihoodDiversification (marketing strategy)Context (archaeology)ChinaUrbanizationAgrarian societyEnvironmental planningSustainable developmentNatural resource economicsBusinessEconomic growthGeographyAgricultureEconomicsPolitical science

Abstract

fetched live from OpenAlex

Recently, increasingly sophisticated studies have investigated the relationship between agrarian livelihoods and the environment, as well as rural–urban interactions in developing countries. The policies developed to respond to these dynamics can constrain livelihood options or provide additional opportunities. In the present study, using a modified version of the telecoupled sustainable livelihood framework to generalize dynamic livelihood strategies in the context of rural–urban transformation and by focusing on recent research in China, we review important factors that shape rural livelihood strategies as well as the types of strategies that typically intersect with livelihood and environmental dynamics. We then examine telecoupled rural–urban linkages given that the dynamics of the livelihood strategies of farmers can cause flows of labor, capital, ecosystem services, and other processes between rural and urban areas, thereby placing livelihood strategies in a dynamic context, which has not been considered widely in previous research. We show that most previous studies focused on the reduction of environmental impacts via livelihood diversification and rural–urban migration. We propose several areas for future policy development and research.

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)0.001
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 score0.102
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.002
GPT teacher head0.214
Teacher spread0.212 · 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

Citations26
Published2019
Admission routes1
Has abstractyes

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