Biodiversity Education as a Catalyst for Change: Exploring Case Studies from Asia and Reflections from Around the World
Bibliographic record
Abstract
I. Why do we need to consider biodiversity and education?The Earth is currently facing a triple planetary crisis-climate change, biodiversity loss, and pollution and waste (UNEP 2024).Addressing these interconnected challenges and accelerating progress toward solutions is essential for a sustainable future.In addition, the United Nations has declared the years 2021 to 2030 as the "UN Decade on Ecosystem Restoration" (UNEP & FAO 2021).These trends underscore the urgency of recognizing biodiversity as one of the world's most pressing global issues.Adopted in 2022, the Kunming-Montreal Global Biodiversity Framework (GBF) presents a new international roadmap to halt biodiversity loss and place nature on a path to recovery by 2030commonly referred to as achieving "nature positive" (CBD 2022).Biodiversity loss is now widely recognized as a global concern, and has been ranked among the top long-term risks by the World Economic Forum (World Economic Forum 2025).Within the GBF, education is given one of the central roles.The framework states: "Implementation of the Framework requires transformative, innovative and transdisciplinary education, formal and informal, at all levels" (CBD 2022).Education is emphasized across multiple sections, including the action targets, implementation considerations, and sections on communication and awareness, underscoring its critical importance.Most recently, at the Convention on Biological Diversity's COP16 held in October 2024, the development of "the Global Plan of Action for Education on Biodiversity" was formally adopted.The plan is currently being developed under the UNESCO and other international partners, underscoring the growing global prioritization of biodiversity education.Transformative education is also seen as a driver of broader societal change.According to the IPBES Transformative Change Assessment, "Transformative change is urgent, necessary and challenging -but possible" (IPBES 2024).Facilitating the sharing of knowledge and practices among educators can help catalyze collaborative action.Considering this international context, it is essential to rethink the relationship between biodiversity and education and to strengthen efforts toward achieving a nature-positive future.Because biodiversity and education are deeply influenced by local ecosystems and cultural contexts, respecting these differences while pursuing shared global objectives is essential.This special issue brings together contributions from educators and researchers working across Asia, exploring the diverse intersections of biodiversity and education in their respective regional contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".