The Emotional Experience of Sustainability Courses: Learned Eco-Anxiety, Potential Ontological Adjustment
Bibliographic record
Abstract
The knowledge content of university-level introductory sustainability courses elicits emotional reactions by students that are novel within the typical classroom context. Common negative reactions include ‘sadness’, ‘worry’, ‘guilt’ and ‘disgust’, while more positive responses include ‘feeling angry’, ‘empowered’, ‘like trying to make a difference’ or ‘having raised awareness’. These emotions are indexical of a deeper social epistemic collision between historically established social identities, including behavioural scripts consistent with, and generative of, unsustainability on the one hand, and a growing collective awareness of the consequent unsustainability that threatens students’ future well-being on the other. The authors argue that introductory sustainability courses set up the potential for not only a learned eco-anxiety, but also an ontological adjustment. That adjustment might bring student, historical inheritance and environment from a state of living in a suffering, but still separate, world to a practice of becoming with a world into which we extend and that also extends into us. Therefore, it is arguably important for instructors to be aware of the possibility of students getting into a negative state of eco-anxiety and for instructors to also have some tools for supporting a more positive ontological adjustment. We recommend that they become skilled in facilitating transformational learning by including some discussions about the ontology of self in any introductory sustainability instruction. Directing students’ attention to their own emotional responses can also be useful for grounding such classroom discussions and transformational learning.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".