In Search of Eco-Democracy: Education for Mutually Beneficial Flourishing
Bibliographic record
Abstract
Abstract This paper begins with crises; environmental, social and democratic. And then it posits that in the midst of these crises there might be an opportunity. One that involves not so much “saving” democracy and sustaining current ways of life but shifting attentions towards potentially creating (re-creating) something different. Something we are calling eco-democracy. There have long been voices, calling for a more environmentally thoughtful form of democracy. After tracing a short discussion of this history including some of the critiques we turn to an exploration of eco-democracy in environmental education. Our argument is that some forms of environmental education are already thinking in more eco-democratic ways without necessarily naming the project as such. In order to do this, we focus on five ‘seedlings’ of eco-democracy that already exist in environmental education. These seedlings allow us to do two things. First, draw connections to Wild Pedagogies and second draw out four key considerations for environmental educators if they are interested in having more eco-democratic practices: voice, consent, self-determination and kindness. The paper ends with a short speculative exploration of what might happen pedagogically if environmental education were to assume an eco-democratic orientation through honouring voice, consent, self-determination, and kindness.
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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 | 0.010 | 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 | 0.006 | 0.022 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".