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Record W4400962595 · doi:10.1080/15575330.2024.2382183

Breaking path dependency? Factors to enhance capacity for rural local governments in Newfoundland and Labrador, Canada

2024· article· en· W4400962595 on OpenAlexafffundabout
Joshua Barrett

Bibliographic record

VenueCommunity Development · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of CanadaOntario Agri-Food Innovation Alliance
KeywordsLocalismDiversification (marketing strategy)Dependency (UML)Economic growthLocal governmentDevelopment economicsEconomicsBusinessPolitical sciencePublic administrationPolitics

Abstract

fetched live from OpenAlex

Path dependency, often coinciding with the downloading of various responsibilities with reduced funding from higher levels of governments during the neoliberal era, has led to capacity challenges for rural local governments to pursue sector diversification activities. Despite these challenges, research has indicated that, through entrepreneurial efforts, breaking path dependency is possible. Drawing from Staples Theory, Evolutionary Economic Geography, New Public Management, and New Localism as well as primary data from key informant interviews, this paper identifies five factors that influence the capacity of rural local governments to break path dependency. In doing so, it identifies a relationship between New Localism and local governments, and its potential as a bridge for rural economic development. These findings are important, as they contribute to the limited but growing literature related to New Localism and its potential applications for rural local governments in Canada to facilitate economic development.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.359

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.006
Scholarly communication0.0040.001
Open science0.0010.004
Research integrity0.0010.002
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 head0.017
GPT teacher head0.234
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 designQualitative
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

Citations3
Published2024
Admission routes3
Has abstractyes

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