MétaCan
Menu
Back to cohort
Record W2066461185 · doi:10.5509/2012854767

Making Climate Change Policy Work at the Local Level: Capacity-Building for Decentralized Policy Making in Japan

2012· article· en· W2066461185 on OpenAlexvenueno aff
Yasuo Takao

Bibliographic record

VenuePacific Affairs · 2012
Typearticle
Languageen
FieldDecision Sciences
Topicdemographic modeling and climate adaptation
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Policy makingClimate changeClimate policyCapacity buildingBusinessPolitical scienceEnvironmental planningPublic administrationGeographyEngineeringGeology

Abstract

fetched live from OpenAlex

This study will examine the state of local capacity building for local climate adaptation in Japan. Climate mitigation needs to be led by both global strategies and national mandates in an integrated way, but climate change impacts are manifested locally and adaptive capacity is determined by local conditions. The article first lays out the basic components of local capacity for decentralized policy making and assesses the current local capacity in view of Japan's climate policy. The bulk of data employed in the study is derived from existing up-to-date government databases. It found that only the largest municipalities as well as prefectures have governing capacities to develop a comprehensive approach to climate adaptation, while medium-sized municipalities have a potential to take a participatory approach to climate policy. It argues that some pioneering localities realize their potentials to take initiatives under political leadership but most localities act in a piecemeal fashion according to clear national-level guidance on climate change.

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0050.004
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.269
GPT teacher head0.412
Teacher spread0.143 · 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 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

Citations15
Published2012
Admission routes1
Has abstractyes

Explore more

Same venuePacific AffairsSame topicdemographic modeling and climate adaptationFrench-language works237,207