Time for a vision exam: diagnosing problems in the pursuit of equitably transformative resilience in food systems
Bibliographic record
Abstract
Amid intensifying climate change, biodiversity collapse, political instability, and widening inequality, the urgency to reimagine food systems is greater than ever. This commentary builds on the concept of Equitably Transformative Resilience (ETR), first proposed in the 2025 High Level Panel of Experts on Food Security and Nutrition (HLPE-FSN) report Building Resilient Food Systems (HLPE, 2025). As elaborated in the report, conventional approaches to resilience emphasize “bouncing back,” through privileging risk management and return to its prior state, reinforcing the very structures that generated vulnerability. These framings obscure ecological fragility and entrenched inequities, leaving communities and ecosystems unable to achieve genuine resilience. Drawing on the HLPE report and the wider literature, we use the metaphor of a vision exam to identify six common ways that dominant resilience framings are distorted in relation to food systems: (1) tunnel vision (siloed thinking), (2) bifocalism (separating ecological and social dimensions), (3 and 4) temporal myopias (ignoring historical injustices and short-termism), (5) spatial myopia (overlooking cross-scale dynamics), and (6) overlooking intersectionality. Correcting these distortions illuminates pathways toward ETR.
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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 | 0.018 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.048 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.014 | 0.020 |
| 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".