Biodiversity Conservation and Environmental Education in Asia: Case Studies, Opinions, and Suggestions for Our Future Endeavors
Bibliographic record
Abstract
I. Context and focusIt is our pleasure to announce the publication of the fifth issue of Environmental Education in Asia (EEA), the English issue of the Japanese Journal of Environmental Education (JJEE).Discussions regarding the launch of Environmental Education in Asia began in 2014, and with the publication of its first issue in 2017, we have been exploring the potential of collaborative efforts to foster environmental education across Asia.We envisioned EEA as a platform for researchers who are not only conducting research in their own countries but are also keen to engage in international interactions and contribute to mutual and global understanding (Sakurai & Furihata 2019) under the theme of environmental education in Asia.Although more than 40 papers have been published in the past four issues, we believe that international and collaborative efforts between practitioners and researchers from various countries across Asia in the context of environmental education are limited.This highlights the need to continue developing EEA to promote diverse practices and research findings that emphasize how environmental education can contribute to creating a sustainable society.The fifth issue of EEA features papers on biodiversity conservation and environmental education in Asia.The 15th Conference of the Parties to the United Nations Convention on Biological Diversity held in 2022 adopted the Kunming-Montreal Global Biodiversity Framework, which includes 23 action goals.The framework states that implementation requires transformative, innovative, and transdisciplinary education, both formal and informal, at all levels.It also emphasizes encouraging and enabling individuals to make sustainable consumption choices by improving their access to education as well as accurate and appropriate information and alternatives.In addition, the United Nations has declared 2021-2030 as the "UN Decade on Ecosystem Restoration," recognizing not only conservation but also the restoration of biodiversity as an international goal.In line with these global objectives, the Japanese government has promoted the concept of "Nature Positive" and has been encouraging stakeholders to take urgent action to halt and reverse biodiversity loss, putting nature on a path to recovery.Although similar projects are being implemented across Asia, efforts to summarize and integrate the accomplishments in these regions are limited.II.Structure of the "JJEE-EEA 2025" issue This issue begins with an article by Ma et al. (2025) that provides valuable insights and suggestions for nurturing the next generation of conservationists in Asia.Huang (2025) introduces a remarkable ocean education program at an elementary school in Taiwan.The following paper by Southey (2025) explains the significance of Nature-based 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 | 0.019 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".