When Innovative Institutions Fail: Quest University, Partnerships, Financial Sustainability
Bibliographic record
Abstract
How should innovative institutions respond to financial challenges? The idealism that drives curricular and pedagogical innovation may create inspiring post-secondary institutions; however, institutional sustainability requires commensurate attention and care to the practical realities of their business models. This chapter examines the case study of the financial challenges of Quest University Canada, located in Squamish, British Columbia, with an emphasis on partnerships. Founded with a vision to reshape higher education, the university welcomed its first class in 2007. Enrolment grew over the following years, but not enough to avoid annual operating deficits. In 2020, Quest filed for insolvency protection and restructured through a partnership that extended the life of the institution before forcing it into suspending academic programming in 2023. This most recent partnership is examined in detail and contextualized in the growth of for-profit companies partnering with not-for-profit institutions through service provider agreements. The chapter concludes with practical advice for institutional leaders evaluating their own institutions. In short, the chapter argues that it is not enough for innovative and progressive institutions to hold to their educational mission to provide radically student-centred learning: they need to do so in a way that is financially viable.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".