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Record W2463116585 · doi:10.1177/0899764016655620

Supported Social Enterprise

2016· article· en· W2463116585 on OpenAlexaffabout
Andrea Chan, Sherida Ryan, Jack Quarter

Bibliographic record

VenueNonprofit and Voluntary Sector Quarterly · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSocial enterpriseSocial WelfarePublic relationsSocial workWelfareBusinessGovernment (linguistics)Social organizationSociologyEconomic growthEconomicsPolitical scienceMarket economySocial science

Abstract

fetched live from OpenAlex

This article presents a study of supported social enterprise, a hybrid organization that not only either employs or trains members of marginalized social groups, often on disability pensions and social assistance, but also has social welfare characteristics. These organizations sell services and goods, like other forms of social enterprise, but rely heavily on external support from government programs, foundations, and a parenting nonprofit. The article presents an empirical study using a survey and interviews of participants in these organizations from Ontario, Canada, and notes that even though they earn minimally from work in these organizations, they view the experience positively. The final discussion centers on the concept of supported social enterprise and raises the question as to whether such organizations should be viewed primarily as a form of social enterprise or as a modified form of social welfare organization.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0040.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.033
GPT teacher head0.229
Teacher spread0.196 · 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 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

Citations36
Published2016
Admission routes2
Has abstractyes

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