KNOWLEDGE MANAGEMENT FOR LAND DEGRADATION MONITORING AND ASSESSMENT: AN ANALYSIS OF CONTEMPORARY THINKING
Bibliographic record
Abstract
ABSTRACT It is increasingly recognised that land degradation monitoring and assessment can benefit from incorporating multiple sources of knowledge, using a variety of methods at different scales, including the perspectives of researchers, land managers and other stakeholders. However, the knowledge and methods required to achieve this are often dispersed across individuals and organisations at different levels and locations. Appropriate knowledge management mechanisms are therefore required to more efficiently harness these different sources of knowledge and facilitate their broader dissemination and application. This paper examines what knowledge is, how it is generated and explores how it may be stored, transferred and exchanged between knowledge producers and users before it is applied to monitor and assess land degradation at the local scale. It suggests that knowledge management can also benefit from the development of mechanisms that promote changes in understanding and efficient means of accessing and/or brokering knowledge. Broadly, these processes for knowledge management can (i) help identify and share good practices and build capacity for land degradation monitoring at different scales and in different contexts and (ii) create knowledge networks to share lessons learned and monitoring data among and between different stakeholders, scales and locations. 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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".