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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 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
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 the C anadian 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 the C anadian 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.

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)0.005
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.871
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.171
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