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Record W6986850449

Retaining Financial Capital for Rural Community Development: A Case Study of the Town of Olds, Alberta

2017· article· en· W6986850449 on OpenAlexaboutno aff

Bibliographic record

VenueAgEcon Search (University of Minnesota, USA) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial capitalLocal currencyCapital (architecture)Investment (military)Local communityRural communityCommunity developmentSocial capitalCurrency
DOInot available

Abstract

fetched live from OpenAlex

Financial capital is an important component of rural community development and a key aspect of community resilience. Yet residents often transfer their wealth into investment vehicles such as GICs and bonds that are external to their community. This exodus of financial capital is often in contrast to a deep commitment to the local community in which these residents lived and worked for the majority of their lives. With a focus on the Town of Olds, Alberta, this project seeks to understand the possibilities for local financial capital retention for community development. We compare several approaches to capital retention that include the transition towns movement, community currency and community bonds; we explore perspectives from municipal, provincial, and federal levels of government; we seek insights from the representatives of local financial institutions; and we survey residents of the Town of Olds about their views on local investment. Results indicate a willingness to invest locally among residents, with support from town leaders, governments, and financial institutions. Yet several key barriers exist. These barriers include a limited understanding of financial vehicles for local investment (e.g., community bonds) and the availability of other attractive non-local options to secure financial capital (e.g., loans at attractive rates).

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 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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.288
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.067
GPT teacher head0.301
Teacher spread0.235 · 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 teacher head, not a consensus.

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
Published2017
Admission routes1
Has abstractyes

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