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Record W2792383179

Elite Capture in the Social Enterprise Sector: Examining the Impact of Community Wealth on Social Enterprise Funding in Western Canada’s Metropolitan Areas

2013· article· en· W2792383179 on OpenAlexaboutno aff
Nemanja Jevtovic

Bibliographic record

VenueuO Research (University of Ottawa) · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsnot available
Fundersnot available
KeywordsMetropolitan areaSocial enterpriseEliteEconomic growthBusinessPolitical scienceEconomicsGeographyPoliticsPublic relations
DOInot available

Abstract

fetched live from OpenAlex

Social Enterprises are unique organizations, differentiated from for-profit firms and traditional nonprofits by their pursuit of social objectives through participation in the market. Although these organizations have existed in some shape or form for many years, the reorganization of the welfare state has had an important impact on Social Enterprises. One effect has been the increasing penetration of elites in the many types of Third Sector organizations. This paper outlines the Social Enterprise sector in Canada and finds some support for the existence of Elite Resource Capture in four major cities in Western Canada. Using Census data and survey data of Social Enterprises in Vancouver, Victoria, Edmonton and Calgary, different measures of community wealth are seen to positively correlate with the ability of Social Enterprises to obtain non-earned income sources such as grants, loans and donations. Additional positive correlations are found between the different measures of community wealth and measures of organizational strength, such as the age of Social Enterprises and their number of full-time employees. Three policies are suggested to ensure a better distribution on non-earned income sources across the sector.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.149
GPT teacher head0.334
Teacher spread0.185 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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