Exploring the Impact of Performance-Based Funding Policy Reform: The Role of Institutional Research in Supporting Data-Driven Decision-Making
Bibliographic record
Abstract
The institutional pressures placed on the Ontario college system, exercised through funding model reform, brought forward organizational challenges difficult for even the most fiscally savvy to navigate. The enrollment corridor mechanism and the expansion of the proportions of the differentiation envelope to create a performance-based grant, implemented via the 2020-25 Strategic Mandate Agreement (SMA3), demonstrate the Provincial Government’s calls for efficiencies and accountability and the alignment of institutional and provincial priorities. Remaining financially sustainable while moving from performance reporting to performance funding and weathering the impacts of the Covid-19 pandemic requires a solid understanding of not only enrollment challenges and opportunities but also data and information used to inform decisions. Institutional Research (IR) units are responsible for providing leaders with data and information for this work. However, access to data and information does not imply their effective use (Marsh et al., 2006), pointing to a gap in data literacy skills amongst higher education leaders (Mathies, 2018). The problem of practice that will be examined is the role of IR in supporting effective data-driven decision-making related to achievement of the College X enrollment and SMA3 priorities. This Organizational Improvement Plan proposes that an existing Strategic Enrollment Management governance structure be leveraged for development and implementation of a group-level capacity building strategy. The planned change is used to inform enhancements to existing data tools and resources responsive to stakeholder needs and mindful of organizational context. The Change Path Model (Cawsey et al., 2016) provides the framework to implement this solution using distributed and adaptive leadership approaches.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".