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Record W2741257762 · doi:10.1177/106169340901800202

Food and Non-Alcoholic Beverage Sponsorship of Sporting Events: The Link to the Obesity Issue

2009· article· en· W2741257762 on OpenAlex
Karen Danylchuk, Eric MacIntosh

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

VenueSport Marketing Quarterly · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsIntertek (Canada)
Fundersnot available
KeywordsDemographicsObesityBusinessMarketingAdvertisingGovernment (linguistics)Food scienceEnvironmental healthMedicineSociology

Abstract

fetched live from OpenAlex

This study's primary purpose was to examine the opinions of consumers toward the appropriateness of food and non-alcoholic beverage sponsorships of sporting events in relation to other products. Research of this nature is particularly timely in light of the current obesity issue because many food and beverage products contribute to the obesity problem. Phase one involved a written survey ( N = 253) whereas phase two involved two focus groups ( N = 12). Attitudes toward food and non-alcoholic beverage sponsorships of sporting events were more favorable than alcohol sponsorships, followed by tobacco sponsorships. However, there were differences according to demographics. Overall, sporting goods companies and sport drink and water companies were considered the most appropriate sponsors. Tobacco was the least appropriate sponsor followed by liquor and fast food. The majority of participants were not in favor of government laws to prevent less healthy food and beverage companies from sponsoring sporting events.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.013
GPT teacher head0.235
Teacher spread0.222 · 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