A Case Study of Influence Over a Sponsorship Decision in a Canadian University Athletic Department
Bibliographic record
Abstract
Because of substantial financial cutbacks, Canadian university athletic departments are facing increased pressure to realign their budgets and seek funding from nontraditional sources. Research that addresses influence over funding decisions in university athletics is therefore warranted. This study addressed the attributes of those who are perceived to have influenced an exclusive sponsorship decision, the methods of influence used to influence this decision, and the extent to which athletic department policies and procedures influenced the process. A single-case study in the athletic department of a Canadian university was undertaken to address these questions. The study involved semistructured interviews with coaches and administrators, participant observation, and document analysis. The results indicated that structural factors (i.e., positional power, coaching high-priority sports) had the greatest influence over the funding decision studied, although personal factors (i.e., expertise, personality, seniority) were also key sources of influence. Interactions among the sources of influence were also observed.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.008 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".