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Record W2462947007 · doi:10.22230/cjnser.2016v7n1a207

Allowing Charities to “Do More Good” through Carrying on Unrelated Businesses

2016· article· en· W2462947007 on OpenAlexaffvenueabout
Tamara Larre

Bibliographic record

VenueCanadian journal of nonprofit and social economy research · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsHumanitiesPolitical scienceLegislationRevenueBusinessLawArtFinance

Abstract

fetched live from OpenAlex

One way in which charities could increase their positive impact on society is by raising revenue through carrying on a business. Current income-tax legislation in Canada, however, restricts the ability of charities to do so by prohibiting them from carrying on an unrelated business. This article reviews the current law and explores the options for loosening this restriction, while at the same time addressing the potential problems associated with charity-operated businesses. In the end, the author recommends that all charities except private foundations be permitted to operate small businesses, so long as the business activities are disclosed to donors. Les œuvres caritatives pourraient augmenter leur impact positif sur la société en faisant accroître leur revenu au moyen d’une activité commerciale. Au Canada, cependant, la loi actuelle de l’impôt sur le revenu restreint la liberté des œuvres caritatives en les interdisant de gérer un commerce sans lien avec leur activité principale. Cet article passe en revue la loi actuelle et explore les options pour libéraliser la loi, tout en recensant les problèmes potentiels associés aux commerces qui seraient gérés par des œuvres caritatives. Au bout du compte, l’auteur recommande que toute œuvre caritative à l’exception de la fondation privée ait la permission de tenir une petite entreprise, en autant que l’œuvre mette ses donateurs au courant de son activité commerciale.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.546
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.180
GPT teacher head0.342
Teacher spread0.163 · 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 designTheoretical or conceptual
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

Citations1
Published2016
Admission routes3
Has abstractyes

Explore more

Same venueCanadian journal of nonprofit and social economy researchSame topicCommunity Development and Social ImpactFrench-language works237,207