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Record W1847366112 · doi:10.1111/ropr.12002

Assessing Policy Capacity for Climate Change Adaptation: Governance Arrangements, Resource Deployments, and Analytical Skills in<scp>C</scp>anadian Infrastructure Policy Making

2013· article· en· W1847366112 on OpenAlexaff
Jonathan Craft, Michael Howlett, Mark Crawford, Kathleen McNutt

Bibliographic record

VenueReview of Policy Research · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsUniversity of ReginaAthabasca UniversitySimon Fraser University
Fundersnot available
KeywordsMandateAdaptation (eye)Corporate governanceClimate changeBusinessPolicy analysisClimate change adaptationResource (disambiguation)Set (abstract data type)Environmental resource managementEnvironmental economicsEconomicsPublic administrationPolitical scienceComputer scienceFinance

Abstract

fetched live from OpenAlex

Abstract This article examines the infrastructure policy sector's capacity to respond to climate change adaptation through an analysis of theCanadian case. It includes a three‐level examination of capacity: at the macro level through a virtual policy network analysis; at the meso level through examination of the lead department's evolving mandate and resources; and at the micro level through analysis of survey data related to departmental workers policy tasks and attitudes. Four hypotheses across these three levels are set out and tested at the national and subnational levels. Together, the findings suggest that the policy capacity in theCanadian infrastructure sector will be unable to meet the demands placed upon the sector to respond to the increasing challenges of climate change adaptation.

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)0.008
metaresearch head score (Gemma)0.016
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 score0.990
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.005
Scholarly communication0.0070.004
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.171
GPT teacher head0.498
Teacher spread0.327 · 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

Citations31
Published2013
Admission routes1
Has abstractyes

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