Improving the enabling environment to combat land degradation: Institutional, financial, legal and science‐policy challenges and solutions
Bibliographic record
Abstract
Abstract The need to mainstream land degradation issues into national policies and frameworks is encouraged by international mechanisms such as the United Nations Convention to Combat Desertification (UNCCD) and the Millennium Development Goals (MDGs, 2000). However, mainstreaming has faced a number of interrelated institutional, financial, legal, knowledge and policy barriers. As such, despite 15 years of existence of the UNCCD, successes in reversing and/or preventing land degradation are widely perceived to be limited. This paper highlights the nature of these barriers to mainstreaming and identifies ways in which specific limitations that hamper mainstreaming of land degradation into national, regional and international activities and policies may be overcome. It also identifies institutional infrastructures through which scientific findings may more effectively enter policy, suggesting that scientific bodies are required to strategise, coordinate and stimulate the global scientific research community to support mainstreaming and the up‐scaling of efforts to combat land degradation. Such a scientific body could also stimulate national cross‐sectoral and multi‐stakeholder knowledge exchange. The paper then moves to the national level to examine mainstreaming processes in Namibia, a country particularly advanced in taking a more integrated approach. Although the Namibia case study shows an impressive degree of integration, there are still many lessons to be learned in order to further strengthen mainstreaming processes. These lessons form the basis of our conclusion and recommendations, which outline a potential way forward. Copyright © 2010 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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".