“A Different World is Plantable”. A Case Study of How Alternative Food Initiatives Build Resilience and Promote Food Sovereignty in Franconia, Germany
Bibliographic record
Abstract
It is increasingly recognised that the urgent need for more climate-adapted, context-appropriate and sustainable agro-food systems requires fundamental and crosscutting transformations. Alternative Food Initiatives (AFIs) have been proposed as one actor advancing transformation from the bottom up. This thesis contributes to existing research on AFIs relating to if and how they propose viable alternatives in the agro-food system. To this end, the food production and distribution practices of AFIs were studied through the theoretical lense of resilience and food sovereignty to provide a concrete guideline against which to analyse their practices and thus a comprehensive, nuanced and context-aware understanding, by examining the following research question: How and why do AFIs in Franconia, Germany, build resilience and promote food sovereignty? Through a qualitative multiple case study of ten AFIs using semi-structured interviews and participant observation, and cross-case thematic analysis, common strategies towards resilience and food sovereignty as well as their underlying motivations are identified. The findings show that based on their critique of the conventional agro-food system and primarily through ecological integration, capital development, diversification and social connectivity/networking, the AFIs build resilience to increase their capacity to adapt to change and to self-sustain. Simultaneously and grounded in their building of resilience, they realise their own version of food sovereignty to promote socio-ecological transformation.
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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 | 0.011 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".