Economies of Scale and Scope for Canadian Universities
Bibliographic record
Abstract
We estimate measures of economies of scale and scope for a sample of 48 Canadian universities that produce multiple outputs. Estimates have not been previously attempted for Canadian universities to our knowledge. Declining financial support from provincial governments makes finding cost efficiencies a priority for policy makers. Our approach features two useful innovations: by using panel data for 2011–2019, instead of a cross-section for a single year, there is more variation in the variables to estimate a multi-product trans-log cost function; and we consider the appropriateness of using research funding as a measure of research output by alternatively using publication counts. We did not find economies of scale at any university size but did find ray economies of scale up to 60% of the median university size. Economies of scope were evident up to roughly 1.2 times the median university size. No significant differences in results were found between using publication counts or research funding. Small institutions that cater to different outputs could be merged into comprehensive institutions. The lack of economies of scope for Canada’s larger universities suggests that they could be broken up into smaller specialized institutions if cost efficiencies are a priority.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".