Eco-anxiety and a Desire for Hope: A Composite Article on the Impacts of Climate Change in Environmental Education
Bibliographic record
Abstract
Abstract Responding to increasing concerns regarding human-induced climate change and shared commitment as environmental educators to support climate action, we crafted this article as a composite piece — an emerging method of inquiry. We are eleven contributors: the Editorial Executive of the Australian Journal of Environmental Education and two colleagues who each respond to prompts concerning our experience of climate change and our practices of climate change education. The responses provide insights regarding how we strive to enact meaningful climate action, education, advocacy and agency. This article presents the reader with various ways environmental educators work through eco-anxiety and engage in active hope when supporting climate change education/agency/action. The following insights emerged, illustrating 1. the significance of embracing diverse perspectives and knowledge systems; 2. Emotions as catalysts for action and activism; 3. the value of fostering collaborative spaces/relationships/communities that empower people; 4. the importance of integrating ethical responses and critical climate literacy in climate change education/research; 5. learning from places and multi-species entanglements; 6. acknowledging tensions. We offer these six insights not as a solution but as a potentially generative heuristic for navigating the complexity and uncertainty of climate change education in contemporary times.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".