Environmental education in three German‐speaking countries: tensions and challenges for research and development
Bibliographic record
Abstract
In this article, we explore a series of issues and tensions raised by the papers in this Special Issue of Environmental Education Research. The papers focus on developments in environmental education and ESD research in Germany, Austria and Switzerland. In order to provide an alternative framework for contextualising and understanding the trends and challenges illustrated in the collection, we begin with an examination of Germany’s green political movement, both at the level of national politics but also in relation to broader cultural shifts that have taken place in recent years. We then invite further debate on environmental education and ESD by focusing on three nodes within the discourse on these complex, dynamic and linked fields of theory and practice. First, we explore the themes of compatibility and compliance regarding environmental education and the ‘global’ as two of the key ingredients to ESD. Second, we consider the growing dominance of competency‐based approaches to ESD, primarily in terms of educational standards projects, but also in relation to images of the human therein. Third, we look into understandings of agency in relation to innovation and change, including the role of NGOs in research and policy‐making, and the sources and drivers of possible frames for future research agendas. The article ends by inviting wider discussion and critique of the achievements, tensions and challenges for research and development in environmental education and ESD, both in the three countries, and further afield.
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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 | 0.027 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".