Measuring the Value of Varsity Athletics Through Student Retention and the Balanced Scorecard Strategy
Bibliographic record
Abstract
Abstract This case study determines the value of student athletic programs at Canadian universities, using Vancouver Island University (VIU) as a reference case. First, the study compares Canadian varsity athletics to American collegiate athletics to consider the relative economic value of collegiate athletics between the two countries. Second, the study assesses graduation rates and retention of student athletes in Canadian varsity programs as an indicator of the academic value of these programs. Graduation rates and ten-year trends were found using data retrieved from VIU records of past athletes and the credentials they received. Finally, the Balanced Scorecard (BSC) strategy was also employed to explore ways for institutions to create more value for their athletic departments. The BSC strategy focuses on four main pillars of business that have unique objectives and measures to ensure goals are completed. These strategies can be used to create value by reliably predicting outcomes. Value can be perceived in several non-financial ways from an institutional standpoint, including student success, athletic reputation, or pride in school culture among students and staff. Using the BSC could help Canadian collegiate athletic programs overcome the many barriers that stand in the way of sustainable operations. In this case study, we discovered that, with the overwhelming difference in resources between the US and Canada, Canadian varsity athletics may need to find new ways to engage stakeholders after a year of low activity due to the pandemic. Moreover, results of the graduation data indicate that VIU, the test institution, has had a dramatic incline in academic success since 2016 and that the strategies that have been set in place have fostered a more nurturing educational environment for its athletes. It will be important to continue this type of research among all institutions in the U-Sport/Canadian Collegiate Athletic Association (CCAA) to refresh the current systems in place that yield little return on investment (ROI) for stakeholders. With the suggested strategies in this case study, user groups and stakeholders should see noticeable improvement from four different perspectives: internal, external, financial, and innovation/learning and growth. The BSC is a great tool because it does not require a massive modification of operations. It is a cost-effective solution to scoping in on one area of need at a time if necessary. The information gained may be incredibly useful for determining how to improve one’s athletic department. VIU logo WLCE logo Information Vancouver Island University World Leisure Centre of Excellence © R.E.R. Davidson, 2021
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| 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".