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Record W4317513017 · doi:10.1002/pan3.10425

Understanding China's political will for sustainability and conservation gains

2023· article· en· W4317513017 on OpenAlexaff
Hubert Cheung, Yutong Phoenix Feng, Amy Hinsley, Tien Ming Lee, Hugh P. Possingham, Stephen N. Smith, Laura Thomas‐Walters, Yifu Wang, Duan Biggs

Bibliographic record

VenuePeople and Nature · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsCarleton University
FundersJapan Society for the Promotion of ScienceAustralian Research Council
KeywordsPoliticsSustainabilityLeverage (statistics)ChinaNatural resourcePolitical scienceBeijingEnvironmental resource managementBusinessEconomicsEcologyComputer scienceLaw

Abstract

fetched live from OpenAlex

Abstract Political will is a critical determinant of the success or failure of environmental policies and interventions. Harnessing the political will necessary to implement environmental solutions can be challenging because environmental priorities may compete with other societal interests in policymaking. Environmental solutions are more politically feasible if fundamentally aligned with the core interests of key policymakers. Understanding the political agendas of decision‐makers enables conservationists to identify where political will already exists, and allows environmental objectives to piggyback on the motivation to deliver results. In this paper, we explore the core interests of the Chinese leadership to uncover opportunities to leverage Beijing's political will for sustainability and conservation gains. China's growing influence on ecosystems and natural resource use both within and beyond its borders makes an analysis of its leadership's political will valuable and timely. Read the free Plain Language Summary for this article on the Journal blog.

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 score0.034
Threshold uncertainty score0.231

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 head0.027
GPT teacher head0.256
Teacher spread0.229 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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