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Rural and Remote Municipalities as Practitioners and Intermediaries in Social Enterprise Development: A Case for Place Based Policy

2018· article· en· W2894670986 on OpenAlexaffvenue
Mary Beth Vogel Ferguson

Bibliographic record

VenueRural Review Ontario Rural Planning Development and Policy · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIntermediarySocial enterpriseBusinessRural managementLeverage (statistics)Rural areaSocial capitalEconomic growthPsychological interventionGovernment (linguistics)Public relationsRural developmentMarketingPolitical scienceEconomicsAgricultureGeography

Abstract

fetched live from OpenAlex

Rural municipalities have a critical role to play as intermediaries in the social enterprise development field. This level of government is crucial in the development and maintenance of social enterprise activity in rural and remote contexts, because it is the most reflective of local conditions, concerns, values and histories. Most municipalities do not see or understand how they can leverage municipal and community assets through social enterprise development. There is very little research that supports rural municipalities’ decision-making on their current and potential role as practitioners and intermediaries in rural social enterprise development. This research highlights the effective practices currently in use, or practices that could be replicated to more effectively stimulate and support rural development of social enterprise. And so, this research documents social enterprise interventions as a local phenomenon, but also within an understanding specifically of rural policy, rural opportunities, and rural challenges.

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)0.010
metaresearch head score (Gemma)0.009
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 score0.976
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0180.030
Scholarly communication0.0120.012
Open science0.0020.015
Research integrity0.0040.004
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 head0.029
GPT teacher head0.314
Teacher spread0.285 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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