Higher Education Institutions and the Sustainable Development Goals: Understanding Engagement and Prioritization within Canadian Research-Intensive Universities
Bibliographic record
Abstract
Higher Education Institutions (HEIs) are increasingly coming on board to engage with the United Nations Agenda 2030 - Sustainable Development Goals (SDGs). By leveraging their global connectivity and other advantageous traits, such as their research and knowledge production capability, HEIs are uniquely positioned to be leaders in sustainable strategic planning. To date, however, no systematic attempt has been made to investigate how Canadian HEIs are engaging with the SDGs. In response to this knowledge gap, this research seeks to understand: In what ways are Canadian HEIs (specifically, U15s) engaging with the SDGs, and why? And what SDGs are being prioritized within Canadian HEIs (U15s), and why? This research undertook two phases; first, a content analysis of the HEIs’ strategic documents on SDGs was conducted; and second, an analysis of interviews with participants from the HEIs was undertaken. Findings illustrate how the Canadian HEIs are engaging with the SDGs across many areas of operation from courses and research to community outreach. Results from the document analysis indicate that SDGs 17 (partnership for the goals), 4 (quality education), and 9 (industry, innovation and infrastructure) have higher levels of engagement among the HEIs. Additionally, the interviews provide further context and perspective on the HEIs SDG engagement. These findings can be valuable for future research aiming to provide best practice recommendations to Canadian HEIs, specifically the U15s, to improve their overall SDG engagement. This research also helps to increase overall awareness and understanding of the role HEIs can play in global initiatives.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".