Amplifying actions for food system transformation: insights from the Stockholm region
Bibliographic record
Abstract
Abstract Food is essential to people and is one of the main ways in which people are connected to the world’s ecosystems. However, food systems often cause ecosystem degradation and produce ill-health, which has generated increasing calls to transform food systems to be more sustainable. The Swedish food system is currently undergoing substantial change. A varied set of local actors have created alternative sustainability initiatives that enact new ways of doing, thinking, and organizing. These actors can increase the transformative impact of their initiatives through multiple actions and a variety of amplification processes. We analyzed the actions adopted by 29 food initiatives active in the Stockholm region using information available online. We conducted 11 interviews to better understand the amplification processes of speeding up (i.e., accelerating impact), scaling up (i.e., influencing higher institutional levels), and scaling deep (i.e., changing values and mind-sets). Our results indicated that the initiatives mainly seek to stabilize and grow their impact while changing the awareness, values, and mind-sets of people concerning the food they consume (scaling deep). However, these approaches raise new questions about whether these actions subvert or reinforce current unsustainable and inequitable system dynamics. We suggest there are distinct steps that local and regional governments could take to support these local actors via collaborations with coordinated forms of initiatives, and fostering changes at the municipality level, but these steps require ongoing, adaptive approaches given the highly complex nature of transformative change and the risks of reinforcing current system dynamics.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".