A quest for questions: The JUSTRA as a matrix for navigating just food system transformations in an era of uncertainty
Bibliographic record
Abstract
A just food system transformation is imperative to meet this century's goals of environmental sustainability, economic fairness, and equitable social well-being. While considerations of justice are beginning to inform food system transformation debates, there remains a lack of conceptual and practical integration of these two historically separate disciplinary perspectives. This perspective therefore proposes the just transformation matrix (JUSTRA), which integrates justice and transformation concerns using an interrogative approach. Interrogatives probe the historical, present, and future intersections of justice with specific food system elements. If used conscientiously, the JUSTRA can assist a wide spectrum of food system actors in strategizing, implementing, and monitoring just food system transformations. It can also help stakeholders to more thoughtfully engage with power imbalances both among users and in the food system more broadly—if used "in bona fides." Thus, while further testing is necessary to fully realize the potential of the JUSTRA, the matrix can become a powerful tool in multi-stakeholder dialogues to navigate unpredictable, diverse, and power-laden complexities of just food system transformations.
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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 | 0.030 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.075 |
| Scholarly communication | 0.023 | 0.048 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".