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Record W2801765852 · doi:10.1002/sd.1726

Addressing policy challenges in implementing Sustainable Development Goals through an adaptive governance approach: A view from transitional China

2018· article· en· W2801765852 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSustainable Development · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicChina's Socioeconomic Reforms and Governance
Canadian institutionsCentre for International Governance Innovation
FundersNational Natural Science Foundation of China
KeywordsChinaCorporate governanceSustainable developmentProcess (computing)Set (abstract data type)Economic systemMulti-level governancePolitical scienceEnvironmental resource managementProcess managementBusinessEnvironmental planningEconomic growthEconomicsComputer scienceGeographyManagement

Abstract

fetched live from OpenAlex

Abstract In 2015, the 17 Sustainable Development Goals (SDGs) were adopted by all member states of the United Nations. This paper argues that actually implementing the SDGs is far more challenging than reaching agreement to adopt them. To overcome these challenges, a Four‐Step Adaptive Governance Framework was set up to facilitate implementation of these goals. Two‐level policy tools are presented to illustrate the policy evolution of China's development transitions, within the framework of “adaptive governance.” This study found that the Four‐Step Adaptive Governance Approach is deeply embedded in China's policy process, and it has contributed significantly to China's development during the past four decades. The Adaptive Governance Framework and this example of its application in China suggests it could be a useful tool for developing countries that are formulating their national strategies to achieve their SDGs by 2030.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.076
GPT teacher head0.334
Teacher spread0.258 · 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