The Future of Sustainability Education at North American Universities
Bibliographic record
Abstract
This collection explores sustainability education in the North American academy. The authors advocate for a more integrated approach to teaching sustainability in order to help students address the most pressing problems of the world, embrace experimentation, and foster more meaningful involvement with the communities in which universities are located. Throughout, they remain focussed on identifying opportunities for sustainability in higher education and suggesting specific strategies and tactics to achieve them. Recommendations include pedagogical and structural changes aimed at helping students understand the systems in which they can advance sustainability. This timely volume will be of interest to scholars, academic leaders, policy makers, societal partners in research, and private-sector leaders interested in advancing the sustainability agenda. Foreword by Thomas E. Lovejoy. Contributors: Apryl Bergstrom, Christopher G. Boone, Ann Dale, Thomas Dietz, Roger Epp, Allison F.W. Goebel, Kourosh Houshmand, Robert H. Jones, Naomi Krogman, Shirley M. Malcom, Robert E. Megginson, Patricia E. (Ellie) Perkins, Vicky J. Sharpe, Toddi A. Steelman
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".