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Record W4404233377 · doi:10.70322/rrd.2024.10021

Rural Local Government Institutional Sustainability Programs and Plans in Cascadia: A Comparative Analysis

2024· article· en· W4404233377 on OpenAlexfundno aff
Brent S. Steel, Nicholas P. Lovrich, John C. Pierce

Bibliographic record

VenueRural and regional development · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsnot available
FundersFulbright CanadaOregon State University
KeywordsSustainabilityGovernment (linguistics)Institutional analysisLocal governmentEnvironmental planningBusinessPublic administrationPolitical scienceGeographySociologySocial science

Abstract

fetched live from OpenAlex

This research provides a comparative analysis of institutional sustainability programs in small and rural communities across British Columbia, Oregon, and Washington. The study reveals significant regional differences in the adoption of sustainability initiatives, with Oregon consistently leading in the implementation of various programs such as grant writing, conflict resolution, and e-government. The analysis identifies key factors influencing program adoption, including population growth, economic stability, and remoteness. Communities experiencing significant population growth and financial stability are more likely to adopt multiple sustainability programs, while remoteness and economic challenges, such as inflation, act as barriers. The study underscores the importance of regional context and local conditions in shaping the sustainability efforts of rural communities.

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)0.006
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 score0.255
Threshold uncertainty score0.506

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.037
GPT teacher head0.256
Teacher spread0.219 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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