Convention on Biological Diversity and its protocols: Negotiation, challenges and recommendations on the “capacity-building and development”
Bibliographic record
Abstract
摘要: 能力建设与发展一直是《生物多样性公约》(以下简称《公约》)及其议定书下的重要议题, 《昆明-蒙特利尔全球生物 多样性框架》更是将其作为履约的有效执行手段之一。本文旨在增进生物多样性利益相关方对能力建设与发展问题的全面、 深入理解, 以及探讨为处理这个问题可选择的优先政策事项。首先, 从能力、能力建设和能力发展基本概念入手, 概述了这 些术语在《公约》及其议定书相关文件中的演变历史; 然后, 基于历届《公约》及其议定书的决定, 全面梳理了能力建设与 发展的磋商进程和COP15主要争论焦点, 分析了该领域面临的资金、规划、项目、信息分享以及与其他国际公约之间的协同 等关键性挑战及其原因
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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 | 0.141 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.019 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".