A new convergent science framework for food system sustainability in an uncertain climate
Bibliographic record
Abstract
Abstract The complexity and interconnectivity of food systems and climate requires new thinking and research designs that better address the real‐world challenges of securing the resilience and sustainability of human and environmental systems. Central to such an approach is coherent action across sectors and scales. Although inter‐and transdisciplinary approaches are widely discussed, no convergence model exists to detect and prepare for food system vulnerabilities emerging from disruptions in climate systems, or to address the contributions to climate change from food system functions. Convergence research is critical to solving these vexing dynamics by integrating knowledge from multiple scientific domains to inform societal action. Here, we present a new convergent science model that incorporates four key components at the global, national and local level. Through the newly created Food and Climate Systems Transformation Alliance, we are now operationalizing, testing and refining the model to promote science convergence for tackling systemic vulnerabilities in the current food paradigm. Globally, funding relating to climate change and food systems transformation needs to pivot to support the levels of ambition, magnitude of need and complexity of challenges posed.
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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".