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Record W1605825163 · doi:10.1111/ropr.12000

Policy Capacity and the Ability to Adapt to Climate Change:<scp>C</scp>anadian and<scp>U</scp>.<scp>S</scp>. Case Studies

2013· article· en· W1605825163 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 · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsClimate changeCorporate governanceExtant taxonOrder (exchange)Government (linguistics)ScholarshipEnergy sectorAdaptation (eye)BusinessRegional sciencePublic economicsPolitical scienceEnvironmental resource managementPublic administrationEconomicsNatural resource economicsEconomic growthFinanceGeography

Abstract

fetched live from OpenAlex

Abstract This special issue contributes to extant empirical scholarship assessing governmental capacity to meet significant policy challenges, in this case those related to climate change adaptation. The study includes detailed examination of five policy sectors—finance, infrastructure, energy, forestry, and transportation—in two countries, C anada and the U nited S tates—in order to determine what kinds of governance arrangements and analytical capacities exist in this area, how they are changing (if at all), and how they interrelate with the status and evolution of climate change outcomes in each sector. The articles provide a comprehensive sampling of policy network structure and behavior, organizational mandates and resources, and actual job duties and training of policy actors across these sectors at both the federal and subnational level of government.

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)0.014
metaresearch head score (Gemma)0.076
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.775
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.076
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0020.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.205
GPT teacher head0.479
Teacher spread0.274 · 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