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Record W2131855463 · doi:10.1002/nvsm.405

“Music to our ears”: understanding why Canadians donate to arts and cultural organizations

2010· article· en· W2131855463 on OpenAlexaffabout
Martha Barnes

Bibliographic record

VenueInternational Journal of Nonprofit and Voluntary Sector Marketing · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsBrock University
Fundersnot available
KeywordsThe artsRevenueCultural economicsIncentivePublic relationsContext (archaeology)SociologyMarketingBusinessPolitical scienceEconomicsAccountingLaw

Abstract

fetched live from OpenAlex

Abstract The contribution of donations and volunteer time to North American arts and cultural organizations is impressive. Growing economic uncertainty coupled with the increasingly competitive nature of philanthropic work and fluctuating volunteerism rates describe some of the challenges facing nonprofit managers in the arts and cultural field today. The intent of this study was to explain charitable giving to an arts and cultural organization in a Canadian context using variables supported in the literature related to philanthropic behavior. The variables included the norm of social responsibility, donor benefits, philanthropic behavior, and household income. Data (233 questionnaires) were collected at a renowned community symphony with revenue from various sources including over $1 million annually in private support. Multiple regression analysis determined two of the four hypotheses were supported and two were partially confirmed. While the norm of social responsibility and household income did confirm existing literature, philanthropic behavior seemed only defined by length of time as donor rather than by volunteering for arts and cultural organizations and donor benefits included tax incentives but not receiving a “gift” in exchange for a support. Research such as this, which contributes to our understanding of arts and cultural donors and the benefits they seek, continues to be important with the potential to inform nonprofit managers. Copyright © 2010 John Wiley & Sons, Ltd.

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)0.001
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 score0.113
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.033
GPT teacher head0.306
Teacher spread0.273 · 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

Citations8
Published2010
Admission routes2
Has abstractyes

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