Elevating the role of water resilience in food system dialogues
Bibliographic record
Abstract
Ensuring resilient food systems and sustainable healthy diets for all requires much higher water use, however, water resources are finite, geographically dispersed, volatile under climate change, and required for other vital functions including ecosystems and the services they provide. Good governance for resilient water resources is a necessary precursor to deciding on solutions, sourcing finance, and delivering infrastructure. Six attributes that together provide a foundation for good governance to reduce future water risks to food systems are proposed. These attributes dovetail in their dual focus on incorporating adaptive learning and new knowledge, and adopting the types of governance systems required for water resilient food systems. The attributes are also founded in the need to greater recognise the role natural, healthy ecosystems play in food systems. The attributes are listed below and are grounded in scientific evidence and the diverse collective experience and expertise of stakeholders working across the science-policy interface: Adopting interconnected systems thinking that embraces the complexity of how we produce, distribute, and add value to food including harnessing the experience and expertise of stakeholders s; adopting multi-level inclusive governance and supporting inclusive participation; enabling continual innovation, new knowledge and learning, and information dissemination; incorporating diversity and redundancy for resilience to shocks; ensuring system preparedness to shocks; and planning for the long term. This will require food and water systems to pro-actively work together toward a socially and environmentally just space that considers the water and food needs of people, the ecosystems that underpin our food systems, and broader energy and equity concerns.
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 | 0.026 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.056 |
| Scholarly communication | 0.019 | 0.029 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".