‘What do we talk about when we talk about climate change?’: meaningful environmental education, beyond the info dump
Bibliographic record
Abstract
Abstract Learning about the causes and effects of human-induced climate change is an essential aspect of contemporary environmental education (EE). However, it is increasingly recognized that the familiar ‘information dump delivery mode’ (as Timothy Morton calls it), through which new facts about ecological destruction are being constantly communicated, often contributes to anxiety, cognitive exhaustion, and can ultimately lead to hopelessness and paralysis in the face of ecological issues. In this article, I explore several pathways to approach EE, beyond the presentation and transmission of ecological facts. I position my conceptual discussion around my own teaching experiences speaking about climate change with undergraduate students across several Education classes through 2019 to 2021. I situate these reflections within the current discourse on education and teaching in/for the Anthropocene. Throughout this discussion, I locate various ways in which much EE fails to contribute to student’s agency and empowerment by consistently reducing complex ecological phenomena to a set of problems, mainly economic/technological, to be fixed by technocracy. I propose that a contemplative–existential perspective to EE is capable of responding to these reductions, most basically by providing opportunities and practices for students to process their grief and other emotions through recognizing the Anthropocene as an inescapable reality, but also a reality that cannot be determinately imagined or predicted.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.009 |
| 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".