MétaCan
Menu
Back to cohort
Record W4311325326 · doi:10.54097/hset.v25i.3486

Climate Change Policies of Canada and China

2022· article· en· W4311325326 on OpenAlexaboutno aff
Yifei Wang, Yunqiao Zhan, Qianhan Zhang, Jianqi Zhao

Bibliographic record

VenueHighlights in Science Engineering and Technology · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical economy of climate changeClimate changeChinaCorporate governanceClimate governancePolitical sciencePoliticsDevelopment economicsGeographyEconomicsEcology

Abstract

fetched live from OpenAlex

The climate issue has become one of the topics of common concern today. Therefore, climate change policies have become increasingly crucial in domestic economic development and participation in international affairs. However, throughout the development of climate governance, there are similarities and differences in the formulation of climate policies between east and west countries. This paper chooses Canada as the representative of the western countries and China as the representative of the eastern countries to make a comparative analysis of the two countries’ climate governance process, the domestic climate actions, and the participation in International Climate Governance in recent years and uses the realism theory to explain. This paper finds that due to the differences in economic development and political systems in the history of Canada and China, the climate policies of the two countries develop differently. However, today, when the climate issue is becoming more urgent, the two countries face the common pressure brought by the climate issue and the common interests in solving the climate issue. Their climate change policies are highly similar.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.660

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0060.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.198
Teacher spread0.176 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueHighlights in Science Engineering and TechnologySame topicClimate Change Policy and EconomicsFrench-language works237,207