China and COP 15: a path for responsible environmental power
Bibliographic record
Abstract
The Fifteenth Meeting of the Conference of the Parties to the Convention on Biological Diversity (COP15) will be hosted by China in 2020 and could become a milestone in the history of the Convention.This article aims to identify lessons that can be learnt by China in preparation for COP15.The internal motivations and political gains of several host countries with respect to previous COPs were analyzed by looking at national environmental foreign policies against the backdrop of the country's development and corresponding progress made in convention implementation.This case study of successful COPs indicates that host countries do not treat it as an isolated event but an action under the country's foreign policy strategy, which provides a strong momentum for the country to contribute to the process.Additionally, by formulating host country initiatives in harmony with existing national and regional policies in the field, the host country was able to optimize marginal effects and gains at both the national and global level.China could also make use the opportunity of hosting COP15 to gradually transform its passive and inward-looking eco-environmental foreign policy into an outward-looking one featuring active engagement and work on eco-civilization along with the international community.In preparation for COP15, China should work together with international stakeholders, reinforce regional strategic coordination and synergism with developing countries, and share Chinese experiences in biodiversity conservation in order to contribute to the creation of a fair, rational, and efficient system of global biodiversity governance.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".