A framework for assessing the contribution of alternative food initiatives to food system transformations towards sustainability
Bibliographic record
Abstract
Abstract Alternative food initiatives are vital for transforming food systems towards sustainability by challenging, replacing, and complementing the status quo. Understanding how these initiatives contribute to transformation requires attention not only to their intended outcomes but also to the processes through which these outcomes contribute to broader social change. Existing assessment frameworks rarely consider this intersection. To address this gap, we developed a framework that links outcomes of alternative food initiatives to the processes through which they contribute to transformations, drawing on alternative food literature and prefigurative theorising. We then applied the framework to two alternative food initiatives and found we were able to (1) identify multiple processes through which their outcomes lead to transformation, and thereby (2) develop a better understanding of how different initiatives approach their contributions to transformations differently. This could enhance evidence gathering, assessment, and communication of their benefits, which could be used by change agents to increase public support, advocate for policy change, and access resources. Furthermore, recognising the differences and complementarities among initiatives could also improve the strategic capacity of alternative food movements by fostering appreciation of diverse change-making practices. By providing a tool for assessing the link between outcomes of alternative food initiatives and the processes through which they contribute to transformations, the framework can support researchers and practitioners to foster more coherent and effective transformative action in the food system.
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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 | 0.051 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.025 | 0.013 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".