Integrated land degradation monitoring and assessment: Horizontal knowledge management at the national and international levels
Bibliographic record
Abstract
Abstract The need for improved horizontal knowledge management at the national and international levels is essential for monitoring and assessment of land degradation and desertification. At the national level, governments utilise scientific, socio‐economic and technical data and information for strategic planning, priority setting and national environment and development planning. However, challenges including the lack of capacity and lack of collaboration and sharing of information across governments affect responses to and the effectiveness of monitoring and knowledge exchange, along with the ability to effectively implement treaties. At the international level, a number of Multilateral Environmental Agreements (MEAs) share cross‐sectoral themes related to research and monitoring, information exchange, technology transfer, capacity building and financial resources. The need for increased synergies stems from the similarities between the issues they address. Challenges for improving knowledge management at the international level include insufficient interaction between the scientific bodies of the various MEAs; duplication of reporting, monitoring and assessment efforts; limited knowledge management between the various assessments addressing ecosystems and biological diversity during the past decade; and insufficient collaboration between the United Nations Convention to Combat Desertification (UNCCD), the UN system and the international non‐governmental organisation (NGO) community. This paper examines these challenges and offers recommendations on how monitoring and assessment knowledge can be better managed at the national and international levels. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.020 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".