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Record W4321088151 · doi:10.1080/09640568.2023.2167195

Implementing coastal adaptation: assessing and explaining success by local governments in Nova Scotia, Canada

2023· article· en· W4321088151 on OpenAlexaffabout
David A. Righter, Stephanie E. Chang

Bibliographic record

VenueJournal of Environmental Planning and Management · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNova scotiaClimate change adaptationAdaptation (eye)Action planEnvironmental planningClimate changeGeographyPlan (archaeology)Environmental resource managementPolitical scienceEnvironmental science

Abstract

fetched live from OpenAlex

As coastal communities increasingly plan for climate change, there is a need to understand factors that influence whether planned actions get implemented. This study examines the implementation of coastal adaptation actions across Nova Scotia, Canada, the first province in the country to establish a regional policy framework to address adaptation by requiring municipalities to prepare Municipal Climate Change Action Plans (MCCAPs). Using the MCCAPs from 20 coastal communities, this study employs a mixed-methods approach that includes content analysis, surveys, and expert interviews to follow up on the actions identified as priorities in these plans. It finds that the MCCAPs successfully stimulated coastal adaptation throughout the province: within six years, nearly 75% of the 331 priority actions in these plans were implemented to some degree. Logistic regression models, supported by interviews with municipal representatives, indicate that political continuity and public participation throughout the planning process are significant determinants of successful implementation.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.057
GPT teacher head0.305
Teacher spread0.248 · 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.

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

Citations1
Published2023
Admission routes2
Has abstractyes

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