Benchmarking 10 Major Canadian Universities at the Divisional Level: A Powerful Tool for Strategic Decision Making
Bibliographic record
Abstract
Introduction (1) It has taken almost 30 years for universities to borrow from the corporate world and integrate the concepts, methodologies, and logistics of various quantitative and qualitative evaluative processes (such as evaluation, assessment, and total quality management [TQM]) into institutional planning. It has taken even more time--beginning circa 1980--for performance indicators, strategic planning, benchmarking, and ranking to gain broad acceptance. A rapid review of the recent history and evolution of benchmarking illustrates the exponential growth of its use to compare and rank universities: more than 40 countries (2) now have national or regional university rankings, including * America's Best Colleges published by U.S. News & World Report (see, for example, U.S. News & World Report 2010). * Maclean's University Rankings produced by the Canadian magazine Maclean's (see, for example, Dwyer 2008). * The University Guide published by the Guardian in the United Kingdom. * The CHE University Rankings produced by the Centre for Higher Education Development in Germany. * Six other international university ranking and league tables systems that compare and rank world universities, such as the Academic Ranking of World Universities produced by the Shanghai Jiao Tong University in China and the World University Rankings edited by The Times Higher Education Supplement in the United Kingdom. Very clearly, a change has occurred in university culture: benchmarking is now widely used throughout the world. This cultural innovation necessarily has affected university institutional research activities. At one time, institutional research offices simply produced facts and figures that were collected and published as a fact book, primarily for descriptive purposes. Starting in the early 1980s, data and metrics began to be related to other purposes such as quality improvement, strategic planning, and accountability. These data were then compared to metrics produced by peer institutions. Benchmarking has since contributed to more policy-oriented institutional research studies and has demonstrated the rich possibilities for the use of data analysis and reporting. It was in this context that a consortium of 10 Canadian research-intensive universities launched a data exchange program in 1999 to share information that could be used to identify and evaluate the best practices of each institution and to help each institution position itself strategically to achieve its mission. One part of the program was devoted to collecting departmental-level academic data (instructional and financial) from these 10 institutions. This project built on two previous studies by the consortium that were experimental and limited in focus. In 2001-2002 and 2002-2003, data for six and 12 academic departments, respectively, were collected. In 2003-2004, the goal was more comprehensive: between 30 and 35 academic departments (figure 1) were benchmarked using 24 variables (figure 2) in comparisons based on selected indicators. This article presents the data from 2003-2004 as a case study to illustrate the purpose and methodology (process, variables, indicators, and ratios) of benchmarking. In addition, the article presents the results of this exercise and describes the multiple uses made of the data generated by the program. Figure 2 Variables and Definitions Section 1--Faculty FTE Tenured/Tenure-Track Faculty: Full-time and part-time (converted to FTE) tenured and tenure-track faculty from all funding sources. Filled positions only. Joint appointments have to be prorated. Individuals with duties outside the department such as vice presidents, deans, and associate deans should be excluded for the duties they assume outside the faculty/department but should be prorated for their work within the faculty/department. The count should exclude non-tenured/tenure-track staff. …
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.018 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".