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Record W2045475037 · doi:10.1080/09640568.2013.776951

Incorporating climate change adaptation into local plans

2013· article· en· W2045475037 on OpenAlex
Ian M. Picketts, Stephen J. Déry, John Curry

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Environmental Planning and Management · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsAdaptation (eye)Climate change adaptationClimate changeSustainabilityEnvironmental planningProcess (computing)Environmental resource managementAction (physics)Action planPlan (archaeology)Local adaptationBusinessProcess managementPolitical scienceComputer scienceGeographyEnvironmental scienceEconomicsSociologyPsychologyManagement

Abstract

fetched live from OpenAlex

Local governments can encourage proactive action on climate change by incorporating adaptation measures into long-term planning documents. The authors undertook action-oriented, case study research by participating (as adaptation experts) in the process to create a sustainability and land use policy plan for the City of Prince George, Canada. A range of adaptation measures was incorporated into both documents. Factors enabling the incorporation of adaptation included a high level of local awareness, an existing adaptation strategy to draw upon and the flexible process used to create the plans. Challenges such as a lack of priority, limited policy direction and perceptions of climate change as solely an environmental challenge persist as barriers to incorporating adaptation into local plans, particularly in smaller centres.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.498
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.220
Teacher spread0.203 · 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