An Examination of Consumer Perceptions of Sponsorship Authenticity Between a Major Beer Brand and Professional Women’s and Men’s Hockey Leagues
Bibliographic record
Abstract
Alcohol sponsorship of women’s professional hockey in North America began in 2019, yet men’s professional hockey has received financial support from alcohol brands for decades. The purpose of this thesis was to examine consumer perceptions of sponsorship authenticity as they relate to beer sponsorship of professional men’s and women’s hockey leagues. Moreover, this study examined the differences of gender on perceptions of authenticity towards the two alcohol and sport sponsorships, as well as two correlations: first, between alcohol consumption and sponsorship authenticity, and second, between attitudes towards alcohol sponsorship and sponsorship authenticity. A quantitative, cross-sectional design was utilized, and its sample consisted of Amazon’s Mechanical Turk (MTurk) workers who identified as being legal drinking age in Canada. Respondents were randomly assigned one of the two real sponsorships, which involved the same beer brand, they were prompted with a basic image and brief description of the sponsorship case, and this was then followed by a questionnaire. Results determined that consumers perceived the alcohol sponsorship involving the men’s professional league to be more authentic than alcohol sponsorship involving the women’s league. Regarding gender, males perceived the alcohol sponsorship with the men’s league to be more authentic than its sponsorship with the women’s league, but females exhibited no differences in their perceptions between the two sponsorship cases. A correlation between alcohol consumption and perceived sponsorship authenticity did not exist, while a correlation was found between attitudes towards alcohol sponsorship and perceived sponsorship authenticity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".