Bibliographic record
Abstract
The goals of public higher education are to generate knowledge and transfer knowledge and skills to students to prepare them for making the world a better place. Given current severe threats to human well-being from climate change impacts, biodiversity loss, and global trends of inequality, we will need a strong commitment to sustainability education to achieve that “better place.” This chapter focuses on several key concepts and teaching approaches that can engage students in sustainability challenges and give them some of the necessary knowledge and tools to become thoughtful leaders and followers, problem-solvers, and active citizens. It discusses how key concepts such as intrinsic and extrinsic values help students understand the role of values in human decision-making about addressing bigger-than-self sustainability challenges (e.g., global poverty). The concepts of overconsumption, social commodity chain, metabolic rift, the commons, polycentricity, and resilience allow instructors to traverse disciplines and help students recognize the complex, interdependent nature of social-environmental problems and solutions. The chapter also describes teaching approaches that help students understand how people, problems, and ecological conditions are interconnected and encourage them to move from individual to collective approaches to sustainability. These approaches include place-based experiential learning, project- or problem-based learning, case study conflict studies, collaborative learning, social learning, and community service learning. To effectively engage higher education students in sustainability, educators must provide interdisciplinary and experiential learning experiences and put students in positions where they imagine themselves using innovation, experimentation, trial-and-error social learning, and adaptive management to become future problem-solvers and change agents.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.105 | 0.031 |
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".