MétaCan
Menu
Back to cohort
Record W3037711771 · doi:10.3390/su12125173

Tendencies of Residents in Sanjiangyuan National Park to the Optimization of Livelihoods and Conservation of the Natural Reserves

2020· article· en· W3037711771 on OpenAlexaff
Ting Ma, Kun Xu, Yiming Xing, Hang Shu, Weiguo Sang

Bibliographic record

VenueSustainability · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsUniversity of Alberta
FundersNational Key Research and Development Program of China
KeywordsLivelihoodGovernment (linguistics)National parkBusinessChinaPerceptionEconomic growthSocioeconomicsEnvironmental planningEnvironmental resource managementPublic economicsNatural resource economicsGeographyEconomicsAgriculturePsychology

Abstract

fetched live from OpenAlex

Current research on residents’ ecological protection behaviors commonly adopts the socio-economic approach at the individual level. Yet, such an approach might ignore the impacts of potential psychological factors on resident behaviors, such as on farmers’ willingness and perception to conservation (collectively defined as residents’ tendencies in this study). This research analyzed the factors influencing residents’ preferences for conservation and livelihood trade-off at the community level in Sanjiangyuan National Park, China. We conclude: First, the factors associated with government funding lead to residents’ inclination to trust local government. Subsequently, abundant wealth contributes to the open-mindedness of residents to accept that “changes” are worthy. Second, despite the limited level of education, the fact that residents do not consider terms and conditions of regulations does not hinder recent social and ecological transformation. Third, residents’ comparative support for policies and regulations are commonly related to their interests in livelihood and ecological protection, but the support levels differ among different counties due to geographical and social heterogeneities. Collectively, policymakers should realize the importance of residents’ tendencies as well as their confidence in local government when planning to optimize social ecological transformation policies with a balance between the compensations given and benefits received.

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)0.002
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 score0.018
Threshold uncertainty score0.194

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.056
GPT teacher head0.237
Teacher spread0.181 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueSustainabilitySame topicEconomic and Environmental ValuationFrench-language works237,207