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Record W2586586397 · doi:10.1080/15575330.2017.1285796

Increasing capacity of rural clients to access economic development programs: The Ontario BRE case study

2017· article· en· W2586586397 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCommunity Development · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsMinistry of Agriculture, Food and Rural Affairs
Fundersnot available
KeywordsChristian ministryBusinessEconomic growthAgricultureRural areaCommunity developmentState (computer science)Capacity buildingBusiness developmentPublic relationsMarketingPolitical scienceEconomicsGeography

Abstract

fetched live from OpenAlex

Recognizing the importance of a meaningful business visitation program as a mechanism to monitor the health of a business community, the Ontario Ministry of Agriculture, Food, and Rural Affairs (OMAFRA) has supported rural municipalities and organizations in their implementation of business retention and expansion (BRE) programs since 1998. Since that time, OMAFRA has provided support for over 240 community/volunteer-led projects, with more than 9000 businesses surveyed across the province. As OMAFRA’s clients and the economy continue to evolve, the program has benefitted from a number of updates. This article will examine how OMAFRA adapted its BRE program to enhance the capacity of its rural clientele to undertake the program and implement-related strategic economic development outcomes. These findings can inform other state and provincial-level programs to prepare their clientele to be successful with BRE.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0100.000
Scholarly communication0.0010.000
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.172
GPT teacher head0.384
Teacher spread0.213 · 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