Flow approaches in agri-food systems research: revealing blind spots to support social-ecological transformation
Bibliographic record
Abstract
Agri-food systems are called upon to undergo profound transformation. The development of “flow approaches” (including lifecycle assessment, carbon footprint, ecological footprint, and metabolism methodologies) has been crucial to point to the material side of human activities. More specifically, these approaches highlight the material and energetic costs of long agri-food value-chains, intensive farming practices, high levels of geographic specialization, as well as the production of non-food commodities. In the logical progression from diagnosis to action, flow approaches are currently being used as decision-support tools. But what are the biases induced by flow approaches when it comes to supporting real-world transformations? Based on our experience and interdisciplinary background, we argue that flow approaches provide a decontextualized and narrow framing of issues related to agri-food systems, such as accumulations and transfers across space and time, inequalities and asymmetries along the chain of activities, or long-lasting environmental impacts. Some aspects are measured and emphasized, while others are difficult to observe or neglected. Flow approaches, alone, are not well suited to inform issues of environmental justice, radical transformation, and local governance. As in most cases methodological advances will not suffice to overcome the biases induced, we call for hybridizing methods and for broadening analytical perspectives.
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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 | 0.044 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.013 | 0.032 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".