The Emperor’s New Clothes: Maclean’s, NSSE, and the Inappropriate Ranking of Canadian Universities
Bibliographic record
Abstract
Most Canadian universities participate in the US-based National Survey of Student Engagement (NSSE) that measures various aspects of “student engagement.” The higher the level of engagement, the greater the probability of positive outcomes and the better the quality of the school. Maclean’s magazine publishes some of the results of these surveys. Institutions are ranked in terms of their scores on 10 engagement categories and four outcomes. The outcomes considered are how students in the first and senior years evaluate their overall experiences (satisfaction) and whether or not students would return to their campuses. Universities frequently use their scores on measures reported by Maclean’s in a self-Congratulatory way. In this article, I deal with levels of satisfaction provided by Maclean’s. Based on multiple regression, I show that of the 10 engagement variables regarded as important by NSSE, at the institutional level, only one explains most of the variance in first-year student satisfaction. The others are of limited consequence. I also demonstrate, via a cluster analysis, that, rather than there being a hierarchy of Canadian institutions as suggested by the way in which Maclean’s presents NSSE findings, Canadianuniversities can most adequately be divided into a limited number of different satisfaction clusters. Findings such as these might serve as a caution to parents and students who consider Maclean’s satisfaction rankings when assessing the merits of different universities. Overall, in terms of first-year satisfaction, the findings suggest more similarities than differences between and among Canadian universities.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".