Harvesting change: unraveling social-ecological impacts of a food hub ( LebensMittelPunkt ) through a living lab approach
Bibliographic record
Abstract
In this four-year transdisciplinary study, we assess the contribution of three activities in a food hub (FH) in the form of a living lab to the transformation of the agri-food system of the Berlin-Brandenburg city-region: food delivery from community-supported agriculture (CSA) farms, community dinners in the FH, and food rescuing. We followed, first, a reflexive approach to assess the FH activities’ contribution to the food system transformation: we monitored the flow of all food that arrive at the FH in the three activities. Second, we followed a transformative approach in order to assess how the FH can be scaled (deep, out, and up) to the whole city-region. A multi-method approach based on citizen science principles and applied participatory methods was used to co-design and co-develop the reflexive phase, as well as to co-create the solutions in the following transformative phase considering different actors of the food system. The empirical phase was affected by COVID19-related restrictions, but direct interactions between the actors in a living lab setting were still possible in compliance with hygiene regulations. We found that the CSA deliveries and community dinners were the most impactful activities. The CSA deliveries could already be contributing to meet 50% of current demand of vegetable consumption. We propose a season calendar of regional and seasonal products based on the results from monitoring the CSA deliveries. By combining them with the monitoring of the menus of community dinners, we also propose an increase in the production of specific, relevant products to foster the adoption of sustainable and healthy diets in the region. Overall, we found that the transformative impact of the FH is due to the fact that it is a space that challenges existing power structures, fostering the creation of new visions and shared meaning that deviate from the globalized and conventional agri-food system. We also found that the living lab approach is of key importance for the creation of social-ecological knowledge as the basis for improving the FH activities (deep-scaling). Nevertheless, the multi-method approach allowed us to find that there was still room for taking advantage of the full potential of the FH as a space for social-ecological learning in order to increase its transformative impact. Doing this and scaling the FH concept (LebensMittelPunkt) to the whole city and city-region would contribute to the creation of an agroecology-based territorialized agri-food system. To our knowledge, this is the first study assessing an urban-placed and agroecology-based initiative applying the living lab core characteristics to increase its transformative social-ecological impact. This study could serve to other researchers working with living labs in developing social-ecological transformative solutions.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".