Integrated land degradation monitoring and assessment: Horizontal knowledge management at the national and international levels
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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 it