Bibliographic record
Abstract
This study was designed to uncover the predictions of experts regarding the future of tobacco sponsorship of sport in Canada. The Delphi Technique was used as the research protocol. A census of all marketing managers of tobacco brands involved in sport sponsorship (N = 4) and elite sporting events that utilize sponsorship funds from tobacco companies (N = 7) were involved in the study. Data were collected in three rounds as per the Delphi Technique protocol. In the first Round, each expert answered three open ended questions regarding the future of tobacco sponsorship of sport in Canada. From the responses provided in Round One, eighteen statements were generated that formed the basis for the last two rounds. Responses for the statements on the last two Rounds were evaluated on a 5-point Likert scale for probability, desirability, importance, impact, and priority. The results from Rounds Two and Three lead the researcher to predict that tobacco sponsorship of sport will be severely diminished after the year 2000 and that talent development programs which provide the international events with Canadian sport talent will need to find alternative sponsors if these programs are to survive. Further, many of the current events receiving funding will need to downgrade their events from major international status.Dept. of Kinesiology. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2000 .M352. Source: Masters Abstracts International, Volume: 39-02, page: 0500. Adviser: W. J. Weese. Thesis (M.H.K.)--University of Windsor (Canada), 2000.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".