Teaching in a Time of Climate Collapse: From “An Education in Hope” to a Praxis of Critical Hope
Bibliographic record
Abstract
Given recent geopolitical shifts to abandon an organized response to the climate crisis and the projections of the 2023 IPCC report, scientists have confirmed that climate collapse is likely, if not inevitable. In this perspective paper, we pose two questions: What is the job of a sustainability educator at this point in the climate crisis? What good is hope if the object of hopefulness is not achievable? We examine these questions through a literature review of climate emotions and hope discourse in sustainability education, narrowing our focus to critical hope. Building on existing research, we contend that a sustainability educator’s job in this phase of climate collapse is to convey a praxis of critical hope, which attends to the following realms: (a) the core sustainability curriculum, (b) engagement with emotions and coping skills, (c) the interrogation of complex systems and embedded injustices, and (d) pathways and strategies for organized action. The discussion presented herein analyzes student reflections from a higher-education sustainability course that integrated the principles of critical hope into applied projects. Ultimately, a praxis of critical hope might allow sustainability educators to encounter the dire realities of the climate crisis while sustaining themselves and their students through a long-term labor of love.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.042 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.013 |
| 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".