Performance Measurement in the Public Universities in the Province of Ontario, Canada
Bibliographic record
Abstract
The present thesis aimed to explore the use of performance measurement by the public universities in the Province of Ontario, Canada, primarily from institutional theory and, to a lesser degree, contingency theory perspectives. This examination was based on the most salient relationships identified in the conceptual framework developed by the researcher, which shows how organizational internal and external factors contribute to incentivizing the use of performance measurement by Ontario universities and what the entailed potential consequences are for those organizations. An exploratory case study was conducted using an inductive interpretative approach and mainly qualitative research methods. The researcher utilized reflexive thematic analysis, developed by Braun and Clarke (2006), and he adopted a single case study method with a strategically selected group of 11 Ontario universities being treated as embedded units of analysis. The 43 respondents were targeted mainly at the senior managerial levels of universities, which have the potential to impact the utilization of performance information. The study determined that the use of performance measurement in Ontario universities is significantly encouraged by political and regulatory factors, such as strategic mandate agreements, performance-based funding, and academic accreditation bodies. In addition, larger universities have more resources than the smaller ones to implement sophisticated performance measurement systems, while more complex organizations impose the use of performance measurement to a greater extent than the less complex ones. Furthermore, the use of performance measurement has an important contribution in the process of organizational learning and development by using data to identify areas of strength and weakness in organizations. By communicating them to the public, performance data can reveal the organizational contribution to the community and improve organizational accountability, transparency, and legitimacy. In addition, performance information is a main instrument used in comparisons and rankings of universities, which, in turn, impacts institutional public image. However, participants also unveiled some unintended consequences of using performance measurement. For instance, when organizational focus on performance measurement is only on some targeted domains or when performance indicators are poorly selected by universities or other interested organizations, the global improvement of organizational performance can be adversely affected.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".