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Record W4241833417 · doi:10.24124/2015/bpgub1173

Building a corporate sponsorship plan for a nonprofit arts organization: Prince George Folkfest Society

2015· dissertation· en· W4241833417 on OpenAlexaboutno aff
Aidyl Jago

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilityThe artsPublic relationsGeorge (robot)ManagementSustainabilityNon profitBusiness planPolitical scienceStakeholderProfit (economics)BusinessMarketingPublic administrationEconomicsLaw

Abstract

fetched live from OpenAlex

The purpose of this project was to ensure the long-term sustainability of a non-profit arts organization, Prince George Folkfest Society, by developing its corporate sponsorship capacity, so that it may to continue to contribute to the social and economic betterment of the city of Prince George for its current and future residents and businesses. A Literature Review was conducted on the state of corporate sponsorship in Canada focusing on non-profit arts organizations and outlining the methods and motivations for businesses to engage in corporate sponsorship. To help devise an actionable sponsorship plan for PGFFS, best practices in sponsorship fundraising were analyzed and several business analysis techniques were applied. The research revealed that the study of corporate sponsorship in Canada is in its infancy, and in general, businesses are not highly engaged in corporate sponsorship. Furthermore, professionalism on the part of the non-profit society is crucial in order to retain corporate sponsors. --Leaf ii.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.902

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.001
Scholarly communication0.0050.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.126
GPT teacher head0.341
Teacher spread0.214 · 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 designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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