National Context Impacts on SDG Mapping Needs and Approaches in Higher Education, a Tri-National Comparison
Bibliographic record
Abstract
Since 2015 and the Paris Agreements, several countries have committed to sustainable development (SD) and the Sustainable Development Goals (SDGs). Higher Education Institutions (HEIs) have an important role to play in providing education and supporting research activities that integrate SD and SDG concepts. However, the context where the HEI is located has an impact on the level of development and integration of strategic guidelines, methods, and tools for measuring the performance of SDGs within the HEI. The United Nations framework remains the most developed and used tool, but it stays very global and needs to be adapted to other contexts, which leads to local initiatives by some HEIs in developing their tools. The response of HEIs to this challenge differs from one context to another, and this article aims to (i) provide a framework to analyze the different HEI contexts based on their own global, national, and local context; (ii) present and compare the context analysis of three different HEIs (ULaval, Sorbonne Univ, and UWE) in three different countries worldwide (Canada, France, and England), and (iii) discuss the limits, challenges, and research opportunities in the subject of SDG integration within HEIs. Notably, the context analysis of ULaval and UWE case studies showed that the Canadian and UK scales give global orientations with a delegation to the Quebec Province and England government for the education and research strategies. A strong leadership comes from the HEIs themselves in developing their own methods and tools for assessing and monitoring the SDGs, as is the case with ULaval and UWE. On the other hand, the Sorbonne Univ case follows the French national and European-United Nation framework but is less committed to developing its own tools and methods.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.014 |
| 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".