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Record W4404237874 · doi:10.1111/cag.12960

Regional climate change adaptation planning in Canada: Actors and their articulation

2024· article· en· W4404237874 on OpenAlexaffvenueabout
Sebastian Weissenberger, S. Jeff Birchall

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsUniversity of AlbertaUniversité TÉLUQ
Fundersnot available
KeywordsArticulation (sociology)Adaptation (eye)Climate change adaptationClimate changePolitical scienceEnvironmental resource managementEnvironmental planningRegional scienceGeographyPsychologyEnvironmental scienceGeologyPolitics

Abstract

fetched live from OpenAlex

Abstract Climate change governance presents challenges and most of the responsibility for it is offloaded to local governments often ill‐equipped to deal with it. In Canada, where climate extremes are on the rise, regional adaptation governance structures have emerged as an avenue for more efficient adaptation. Governance at this scale has the potential to mutualize expertise and means, to involve local population, and favour a vertical integration of the adaptation process. From selected examples of regional collaboration on climate change adaptation, in this short viewpoint paper, we identify some factors favouring such initiatives. The presence of “boundary organizations” such as research centres or non‐governmental organizations that can act as catalysts, is a predictor in all our cases and has been proposed in literature before. Funding opportunities can of course offer a strong incentive for various actors to get together. Other factors such as geographical or cultural particularities can shape communities’ response to the stress of climate change. More research should be led into understanding these factors and translate them into policies favouring the emergence of regional adaptation instances, especially in rural coastal zones, in Canada.

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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0010.001
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 head0.068
GPT teacher head0.250
Teacher spread0.182 · 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 teacher head, not a consensus.

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

Citations0
Published2024
Admission routes3
Has abstractyes

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