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Record W4319262789 · doi:10.1111/rsp3.12649

Municipal entrepreneurialism: Can it help to mobilize resource‐dependent small communities away from path dependency?

2023· article· en· W4319262789 on OpenAlexafffundabout
Laura Ryser, Joshua Barrett, Sean Markey, Greg Halseth, Kelly Vodden

Bibliographic record

VenueRegional Science Policy & Practice · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsMemorial University of NewfoundlandSimon Fraser UniversityUniversity of GuelphUniversity of Northern British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDependency (UML)Path dependencyResource (disambiguation)Path (computing)Economic geographyGeographyPolitical scienceEconomic growthComputer scienceEconomicsArtificial intelligence

Abstract

fetched live from OpenAlex

Small resource-based communities across Canada are experiencing rapid change within a volatile, fluctuating global economy. As communities seek to diversify their economies, they are enduring complex provincial and federal neoliberal policy environments that offer fewer funding resources while offloading more responsibilities onto local governments. Drawing upon the case studies of Burns Lake, British Columbia and Grand Falls-Windsor, Newfoundland and Labrador in Canada, we explore how small municipalities are using municipal enterprises to generate revenue and assets that may lead to new economic pathways. Our findings suggest that municipal entrepreneurial strategies are producing mixed success to break from past forest-sector dependencies. These small municipalities still struggle with developmental and operational risks, as well as debates about whether to use revenues to meet new municipal responsibilities and increased demands on aging infrastructure and services versus transformative change.

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)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.005
Scholarly communication0.0060.003
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.104
GPT teacher head0.391
Teacher spread0.287 · 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

Citations10
Published2023
Admission routes3
Has abstractyes

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