MétaCan
Menu
Back to cohort
Record W2726747334 · doi:10.1162/glep_a_00417

Policy Infusion Through Capacity Building and Project Interaction: Greenhouse Gas Emissions Trading in China

2017· article· en· W2726747334 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

VenueGlobal Environmental Politics · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsPrairie Bible Institute
Fundersnot available
KeywordsGreenhouse gasLeverage (statistics)ChinaBusinessJurisdictionProcess (computing)CommissionEmissions tradingCapacity buildingEnvironmental economicsIndustrial organizationEconomicsFinanceEconomic growthPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Capacity-building projects can be a vehicle for fostering policy diffusion. They should not, however, be considered as exclusively externally driven; the receiving jurisdiction’s receptiveness and leverage to steer the design of those projects can be crucial factors, shaping the process of infusing different external policy expertise and experiences into domestic policy design and implementation. This article shows that the Chinese National Development and Reform Commission (NDRC) has played a key role in steering the capacity-building efforts of external financiers in the case of greenhouse gas (GHG) emissions trading. The focus here is twofold: analyzing, on the one hand, the interaction among capacity-building projects financed by different external financiers, and on the other, the role that central actors and brokers can play in the complex structure of interacting projects.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.079
GPT teacher head0.294
Teacher spread0.215 · 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