Contributing to China's Biodiversity Conservation: The Role of Nature Education
Bibliographic record
Abstract
ABSTRACT Ongoing biodiversity loss has far‐reaching implications for human well‐being and survival. To address the accelerating rate of biodiversity loss, the 15th meeting of the Conference of the Parties to the United Nations Convention on Biological Diversity (COP15) prioritized “Strengthening Biodiversity Publicity and Education” as a key agenda item throughout its process, both during the initial phase in Kunming, China, in 2021 and the second phase held in Montreal, Canada, in 2022. Nature education, which has emerged as a useful approach toward achieving sustainable development goals, is intrinsic to raising public awareness about the importance of biodiversity conservation. Despite many countries employing this kind of strategy, nature education was introduced relatively late in China, and its role in biodiversity conservation remains underexplored. Few studies have proposed frameworks for integrating nature education into biodiversity conservation efforts. This study aims to fill this gap by establishing a framework for biodiversity conservation in China that incorporates nature education. It also examines how nature education supports biodiversity conservation, clarifies the relationship between the two, and analyzes the current practices and challenges of nature education in China. The findings provide a reference for developing an efficient nature education system that fosters sustainable interactions between human beings and nature.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".