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Record W4407923500 · doi:10.1080/08941920.2025.2463075

Small and Rural Local Government Environmental Sustainability Plans, Programs and Policies in Cascadia: A Comparative Analysis

2025· article· en· W4407923500 on OpenAlexafffundabout
Erika Allen Wolters, Brent S. Steel, Tamara Krawchenko, Sadaf Farooq

Bibliographic record

VenueSociety & Natural Resources · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainable Development and Environmental Policy
Canadian institutionsFulbright CanadaUniversity of Victoria
FundersFulbright CanadaUniversity of Victoria
KeywordsSustainabilityGovernment (linguistics)Environmental planningLocal governmentEnvironmental policyEnvironmental resource managementBusinessPolitical sciencePublic administrationGeographyEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

Small and rural local governments currently face many ongoing and numerous new challenges that complicate their task of sustaining current public services and programs. How government officials adapt to these changes will affect the long-term viability of local governments in both the U.S. and Canadian contexts. This study examines the presence or absence of environmental sustainability plans, policies, or programs in small and rural local governments in the “Cascadia” region of Canada and the U.S. (British Columbia, Oregon, and Washington). Using surveys and interviews of Cascadia local government leaders during the summer and fall of 2023, correlates of policy adoption are examined, including cultural, demographic, economic, and political factors. Findings suggest that environmental sustainability is a priority in Canada and the U.S. and that overcoming existing challenges including funding, staffing, and political salience, could help facilitate environmental sustainability policies and programs.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.912
Threshold uncertainty score0.226

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.223
Teacher spread0.217 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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