Strategies for campus sport centre funding: a case study on the Charles Jago Northern Sport Centre
Bibliographic record
Abstract
Campus Sport Centres are important for university or college to retain and recruit student body, faculty, staff, and community memberships.It is important for these institutions to implement effective strategic plans that meet the needs of customers and stakeholders while remaining financially independent.The study determines how a campus sport centre can become financially self-sustaining without the need of government funding to support the operations of the facility while delivering value to its customers and stakeholders.The study used the Charles Jago Northern Sport Centre (CJNSC) of the University of Northern British Columbia (UNBC) as the case organization.The strategic priority for the CJNSC is to become a financially self-sustaining facility to cover costs associated with its aging facility.Through the review of secondary data and some primary data, the study identified eight key revenue streams that could be implemented by the CJNSC.These include; 1) increases to the percentage of UNBC student recreation and fitness fees that the CJNSC receives, 2) increased fees to programming and memberships, 3) forming strategic alliances, 4) space utilization, 5) sport tourism planning with seasonality of facility booking space, 6) cancellation fees for memberships, 7) a membership pricing strategy, 8) naming rights.It was determined through the case organization that reducing expenses would not have the long-term benefit for the CJNSC and only existing and new revenues would satisfy the goal of being financially self-sustaining.This information is provided through an integrative framework that could be used as a template or tool by other organizations of similar structure and system as the CJNSC to develop their sports centers for successful outcomes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".