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Record W4225968216 · doi:10.1080/15575330.2022.2059689

Embedding rural capital? Community investment funds in Canada and their implications for rural communities

2022· article· en· W4225968216 on OpenAlexafffundabout
Alex Petric, Ryan Gibson

Bibliographic record

VenueCommunity Development · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsUniversity of Guelph
FundersOntario Agri-Food Innovation Alliance
KeywordsInvestment (military)BusinessRural economicsRural communitySocial capitalCapital (architecture)FinanceEconomic growthRural areaRural developmentEconomicsGeographyPolitical sciencePolitics

Abstract

fetched live from OpenAlex

Canada’s rural areas face economic challenges due to globalization and urbanization. These trends lead to wealth being less “embedded” in place as citizens have fewer geographic constraints and rely more on intangible resources for livelihood. To counter these effects, some Canadian provinces allow the creation of Community Investment Funds, which sell business equity shares to residents. Many such provinces also offer tax incentives to spur investments, and some incentive programs have generated attention, but the potential and impact of these programs is difficult to determine. We presents results from a study of Community Investment Fund programs, including a document review and interviews with key informants from across Canada. By assessing program impacts and qualities, we find that these programs positively impact rural economic and community development but require provincial resources to encourage participation and understanding. Further expansion and resourcing of these program could create positive impacts for embedding capital in rural places, facilitating rural prosperity.

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)0.010
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 score0.106
Threshold uncertainty score0.768

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0110.004
Scholarly communication0.0070.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.081
GPT teacher head0.256
Teacher spread0.174 · 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

Citations6
Published2022
Admission routes3
Has abstractyes

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