“We Don’t Always Have to Be Talking about It”: Moral Reasoning in US Early Childhood Education for Sustainable Development
Bibliographic record
Abstract
The climate crisis is both an environmental and moral issue. The United Nations’ (UN) Sustainable Development Goals (SDGs) provide a framework for a global response to systematically challenge the world’s reactions to the climate crisis, making sustainable education for all a priority. For such sustainability education to be effective, it should engage children in early childhood in, about, and for the environment, emphasizing the moral ramifications of climate equity and justice. We investigated in what ways 19 United States (US) nature-based early childhood educators focused their sustainability education (ECEfS) in, about, and for the environment. The types of activities that engaged about and for experiences were related to the moral principles of welfare, harm reduction, resource allocation, and equality, as well as teachers’ reasoning about these experiences with children. Our findings suggest that educators’ curricula and activities reflect potential moral issues related to sustainable development. However, educators did not engage children in moral reasoning about these issues. A possible explanation is US teachers’ beliefs about developmental practice and children’s capabilities leading them to rarely engage in moral reasoning about sustainability issues instead of scaffolding children to develop personal psychological resources, thereby supporting the SDG for sustainable education.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.013 |
| 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".