Fertile ground? Options for a science–policy platform for land
Bibliographic record
Abstract
The United Nations Convention to Combat Desertification (UNCCD) remains the only ‘Rio Convention’ that is not well served by the scientific community and lacks the equivalent of an IPCC (Intergovernmental Panel on Climate Change) or the proposed IPBES (Intergovernmental Science–Policy Platform on Biodiversity and Ecosystem Services). The mounting pressures on land (and water) that can drive its degradation include population growth and associated food security concerns, over use, creeping degradation, competition between agriculture and renewable energy production, carbon sequestration and land acquisition by foreign entities. These environmental and human pressures clearly require urgent policy attention. We report the results of a survey of the scientific community on the need and possible options for a science–policy platform that focuses on land. The paper then describes the remit and role of an independent platform, the benefits and possible modalities that are inclusive and build on existing institutional structures. Both short-term and longer term options are presented that can respond to immediate needs while establishing a mechanism that can handle the interacting and sometimes overlapping aspects of land covered by other Multilateral Environment Agreements (MEAs). Short-term options include establishing a platform via an ad hoc working group within the proposed IPBES that would feed its outputs into the UNCCD and other relevant MEAs. Long-term options include a more polycentric approach, establishing a network of networks that could evolve into a fully-fledged Independent Platform on Land Degradation given sufficient support, interest and leadership from the international and donor communities.
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.
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 | 0.032 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.023 | 0.043 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".