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Record W2893238929 · doi:10.5539/jsd.v11n5p126

Community Contribution Companies and Access to Social Finance

2018· article· en· W2893238929 on OpenAlexafffundvenueabout
Bridget M Horel, Kevin McKague

Bibliographic record

VenueJournal of Sustainable Development · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsCape Breton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIntermediaryFinanceBusinessInvestment (military)Social capitalIncentiveGovernment (linguistics)Financial intermediaryPublic relationsEconomicsPolitical scienceMarket economyPolitics

Abstract

fetched live from OpenAlex

There is widespread agreement that innovative funding solutions are necessary to address capital requirements of social enterprises, social purpose businesses and not-for-profits in the social economy. The community contribution company (C3) is a legal organizational form for social enterprise, introduced in British Columbia, Canada, in 2013. In creating this legal form, the British Columbia government intended to provide social entrepreneurs with a recognized legal structure designed, in part, to assist social enterprises in gaining access to investment capital. Drawing on interviews from 14 of the 35 registered C3s and a review of filing data, this study provides information to help understand what attracted organizational founders to the C3 model, what challenges are experienced by C3s engaging with investors and financial institutions, and what opportunities there are for improvement to the C3 legal form. This study found that reasons outside a motivation to access investment capital are key driving factors for incorporating as a C3; there is currently a low level of engagement from impact investors; financial incentives may have a role in increasing investment in C3s; and there is an expressed need and opportunity to enhance education about the model to further support C3s. While the consensus from interview respondents was that benefits of incorporating as a C3 outweighed disadvantages, we found that the model has not helped organizations attract social finance and investment. As this model is in relatively early stages of implementation, the lessons learned in this study can inform investors, social finance intermediaries, entrepreneurs, and policy makers.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.490
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.055
GPT teacher head0.289
Teacher spread0.234 · 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.

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

Citations2
Published2018
Admission routes4
Has abstractyes

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