Hard Times and Higher Education: A Brief Comparative Analysis
Bibliographic record
Abstract
The quality of higher education at public universities can be detrimentally affected during financial downturns. Given the recent unprecedented shock to global economy, the current and future quality of publicly funded universities in North America may be at risk in the absence of appropriate and measured policy intervention(s). With these issues in mind, this paper seeks to determine whether and how public higher education institutions are able to withstand these shocks in order to discern whether specific higher education models are better able than others to weather the crisis. In order to address this question, we evaluate the funding transitions of Ontario’s publically funded system (universities only) in comparison to the University of California’s system from November 2008 to November 2009 to capture the magnitude of the global recession’s immediate effects on the provision of higher education. Ultimately, the paper determines whether and to what extent one system has proved more resilient than the other and will seek to address whether path dependence has affected the manner in which each system has responded to the crisis. Both the province of Ontario and the state of California actively chose to make dramatic system design decisions regarding higher education during the early 1960’s. While the structure of each system since that time has diverged considerably, the Ontario universities and the University of California system remain the largest publicly funded higher education schemes in their respective countries. Additionally, the province of Ontario and the state of California have both suffered large budget crises in the past year, yielding significant impacts on their systems of higher education at both the micro and macro levels. The two jurisdictions thus make an interesting and appropriate comparison for studying the effects of the financial crisis upon systems of higher education in the United States and Canada. To assess these impacts, measurements include: changes to funding structures, increases in student to faculty ratio, admittance rates, scholarship provision and changes in the salaries paid to faculty and staff. Additionally, implications of changes in these measurements will be considered for the following groups: students (in particular students of minority or low socio-economic background), faculty and administration, taxpayers and government. Publicly available data, such as the Common University Data in Ontario (CUDO) provided by the Council of Ontario Universities and the statistics available through the websites of the government of California and the University of California, are used to assess which system has proved more resilient to the crisis. Based upon the evidence observed, the paper will address the ways in which specific structural changes made to the publicly funded model as a result of the crisis might play out in the long term for both systems and explain how these modifications will better groom these institutions for future economic shocks. In sum, this project provides a preliminary survey of how the Californian and Ontario systems have each reacted to the recession in order to identify similarities and disparities that could affect each system in future. On the surface, it appears that Ontario's comparative resiliency is attributable to two factors: a sustained/strong commitment to large-scale investment programs, and provincially regulated tuition limits that have prevented universities from abusing tuition as a source of revenue. In contrast, the more centralized system that regulates universities in California seems to be a disadvantage and has motivated deeper systemic cuts. Overall, a discourse of investment (ON) versus expense (CA) is evident and reflective in differing views of PSE; where ON clearly views PSE as an investment in human capital and CA views PSE as expensive to sustain during an economic contraction. Despite being jurisdictional comparisons, the systems differs in terms of institutional structure: California is more centralized and has a Master Plan, whereas Ontario is less centralized and does not have a guiding strategic vision with the comprehensiveness or longevity of California’s. It is unclear if the different reactions of each system to the recession are attributable to these institutional differences. We hypothesize that California's Master Plan has not equipped the state with the flexibility necessary to navigate the challenges of the recession, and that the relative independence of Ontario's individual universities has been an asset. Ontario’s focus on quality and access is a positive attribute and could be a source of competitive advantage in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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".