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Record W2132614126 · doi:10.1123/jsm.27.2.95

Do Charity Sport Events Function as “Brandfests” in the Development of Brand Community?

2013· article· en· W2132614126 on OpenAlexaff
Jules Woolf, Bob Heere, Matthew C. Walker

Bibliographic record

VenueJournal of Sport Management · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPublic relationsEvent (particle physics)Identity (music)Function (biology)AdvertisingSociologyMarketingBusinessPolitical scienceAesthetics

Abstract

fetched live from OpenAlex

Given the ubiquity of charitable organizations and the events used to solicit donations for a cause, many charity-based organizations are continually looking for ways to expand their fundraising efforts. In this quest, many have added endurance sport events to their fundraising portfolios. Anecdotally, we know that building long-term and meaningful relationships with current (and potential) donors is critical for a nonprofit organization’s success. However, there is a paucity of research regarding whether these charity sport events serve as relationship-building mechanisms (i.e., ‘brandfests’) to assist in developing attachments to the charity. The purpose of this mixed-methods investigation was to explore to what extent a charity sport event served as a brandfest to foster a sense of identity with the charity. For this particular case study, the charity event had little effect on participants’ relationship with the charity.

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)0.014
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.005
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.305
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 source (direct Gemma or distilled Codex), 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

Citations65
Published2013
Admission routes1
Has abstractyes

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