Canadian HEIs’ contribution to the SDGs: what do the times higher education impact rankings unveil?
Bibliographic record
Abstract
Purpose This study aims to assess the performance of Canadian higher educational institutions (HEIs) on the sustainable development goals (SDGs) from 2019 to 2023, determine where they have focused on, whether they have improved or not their performance and assess if having larger research income affects their sustainability performance. Design/methodology/approach Data were collected from the Times Higher Education Impact Rankings from 2019 to 2023 on 27 Canadian HEIs, and statistical tests were used to assess performance, trends and significant differences. Findings Results show that while overall Canadian HEIs are performing better on the SDGs, their minimum and maximum scores have improved and the variance across universities has declined; their performance on social and economic SDGs is significantly positive on some but not all these SDGs. More importantly, their environmental performance is poor, with a constant decline in their score on SDG#13 – climate action, which peaked in 2019 and was significantly higher than that of 2023. Results also show smaller research universities perform better than top institutions. Originality/value There is a lot of research on the impact of universities on the SDGs; however, this study makes a deeper and statistical assessment of a quarter of Canadian universities on all the SDGs, with important findings for decision makers to consider as they accept and act according to the role that educational institutions should play in sustainable development.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".