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Record W2890438172 · doi:10.1111/ropr.12309

Do Administrative Traditions Matter for Climate Change Adaptation Policy? A Comparative Analysis of 32 High‐Income Countries

2018· article· en· W2890438172 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

VenueReview of Policy Research · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsMcGill University
FundersPlanbureau voor de Leefomgeving
KeywordsOperationalizationBureaucracyAdaptation (eye)MediationClimate changeConstruct (python library)Political scienceClimate change adaptationPublic administrationPublic economicsEconomics

Abstract

fetched live from OpenAlex

Abstract Although governments are developing and implementing policies to adapt to the impacts of climate change, it remains unclear which factors shape how states are developing these policies. This paper aims to assess whether or not administrative traditions matter for the formation of national climate change adaptation policy in 32 high‐income countries. We operationalize administrative traditions based on five structural criteria: vertical dispersion of authority, horizontal coordination, interest mediation between state‐society, role of public administrator, and how ideas enter bureaucracy. We construct a unique adaptation policy dataset that includes 32 high‐income countries to test seven hypotheses. Our results indicate that countries’ adaptation policies align to some extent with their administrative structure, particularly dispersion of authority and horizontal coordination. However, we find limited evidence that other public bureaucracy factors are related to national adaptation policy. We conclude that administrative traditions matter, but that their influence should not be overestimated.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.838
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.422
GPT teacher head0.588
Teacher spread0.166 · 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