Balanced Scorecard Perspectives on Financial Sustainability of a Small Private University in Canada
Bibliographic record
Abstract
Private higher education institutions have been a focus of scholars because of their increasing closures due to the lack of financial sustainability. Researchers have demonstrated these closures limit society’s choices in higher education and have yet been able to explore the perceptions of leaders of a small private university in Canada regarding their university’s financial sustainability. The purpose of this study was to explore these perceptions using Kaplan and Norton’s balanced scorecard conceptual framework to analyze its four perspectives, particularly its financial perspective. Using the qualitative, descriptive, single case study, data from fourteen leaders were collected from focused interviews. The results of these analyses indicated the importance of international students to grow enrollment, support services needed by the international students, and the agile architecture structures required to provide services such as writing and language support, housing and visa support, and mental health and well-being support. Small private universities in Canada may benefit from the results of this study demonstrating the need for enhanced support services for the international students who are critical to their financial sustainability, and thus the retention of more choices in institutions of higher education in Canada.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".