Designing a Regenerative Future: Higher Education as a Driver of Change
Bibliographic record
Abstract
Many Ontario colleges continue to educate students in the traditional mechanistic fashion of the linear economy. Graduates in search of a career are equipped to perpetuate the take-make-waste economic system that continues to dominate globally and negatively affect our environment and social systems. As knowledge generators and community influencers, higher education institutions can play a significant role in the transition of our current economic model to one that is circular. The future of how Ontario colleges manage its internal operations and design curricula is of paramount importance. However, there is little evidence in the literature to support the transformation process required of higher education to become a supporting structure needed for a circular economy. \n \nSome industry innovators, academics and practitioners are collaborating and experimenting with circular economy. However, too little is happening in Ontario. A shift needs to happen within the Ontario college system. If not, higher education will continue with business-as-usual in developing graduates who do not have circular economy competencies and employers who are all too happy to take them. This paper will enable us to see where Ontario colleges are at today, what they need to be doing for a sustainable and regenerative tomorrow and how they can begin to develop a circular narrative within the college system to support a transition to a circular economy.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.033 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".