A study of performance funding of the Ontario Colleges of Applied Arts and Technology
Bibliographic record
Abstract
In 2000, the Province of Ontario implemented performance funding for community college and universities. The stated impetus behind the implementation of performance funding was to ensure that the postsecondary sector would be held accountable for its use of public resources. This thesis provides an examination of how and in what way the introduction of performance funding has affected the Ontario Colleges of Applied Arts and Technology (CAATS). This study relied on a review of the literature, case studies of four colleges, and interviews with key informants in the college sector, to examine how performance funding has impacted both the day-to-day decision-making and long-term strategic direction of the CAATS. The study found that the community colleges were using the information gathered for performance funding for program review and improvement plans, program rationalization, budget allocations and institutional planning. The colleges were also supportive of the funding for performance policy initiative. This study also examined the process for the implementation of performance funding in Ontario and found that its implementation process and the characteristics of the funding policy shared some characteristics found in other jurisdictions identified as having stable performance funding programs. The results of this study will inform policy and decision makers about the impact of performance funding on postsecondary institutions.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.004 | 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".