Integrating Sustainability into the Curriculum - The Case for Experiential Learning and Applied Student Research at McGill University
Bibliographic record
Abstract
According to the Association for the Advancement of Sustainability in Higher Education (AASHE), sustainability involves meeting the needs of current generations without hindering the ability of future generations to meet their needs (AASHE 2010). Particularly, sustainability can be considered to encompass environmental, economic and social concerns. The environmental or ecological concern arises as the Earth is a closed system with finite resources; the economic or techno-centric concern arises due to limits to human abilities in technology and in the economic system in which it is deployed; and the social or socio-centric concern arises due to society’s need to improve the quality of life of current and future generations (Clift 2007). Understanding sustainability and its tenets is becoming increasingly important as many of the systems that have traditionally been viewed as resilient (e.g. financial markets, fish stocks, climate regimes, etc.) are beginning to fail with social, environmental and economic repercussions. Thus, sustainability and sustainable development have become popular discourses, seen as viable solutions to the failure of these systems. To that extent, sustainability is no longer a niche term relegated to the environmental sector. It has become a crucial concept in economic and social spheres, both of which are intrinsically linked to the environment (Brown et al. 1987).
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 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".