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Leveraging Charity Sport Events to Develop a Connection to a Cause

2017· article· en· W2609308322 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEvent Management · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of WindsorUniversity of OttawaUniversity of Waterloo
Fundersnot available
KeywordsEvent (particle physics)PsychologyPublic relationsTheme (computing)Social psychologyMarketingBusinessPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Charity sport events can be strategically leveraged to provide benefits beyond the event itself. This study explores how charity sport events can be leveraged as an opportunity for nonprofit organizations to stimulate participants' interest in their other cause-related activities. Specifically, the relationships between motives for participation and future intentions to engage in additional cause-related activities are examined. Questionnaires were used to collect data at three separate and uniquely themed running events in support of charities tied to Alzheimer's disease, anaphylaxis, and mental health. Results from the multiple regression analysis highlight the predictive importance of cause, social, and event theme as predictors of future intentions. The physical aspect of the event was an important factor in attracting participants to the event but not predictive of future intentions to engage in additional charity-related activities.

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.

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)0.000
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 score0.269
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.058
GPT teacher head0.364
Teacher spread0.305 · 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