Solidarity Purchasing Groups in Italy: A critical assessment of their effects on the marginalisation of their suppliers
Bibliographic record
Abstract
Over the last twenty years, Alternative Food Networks (AFN) have become increasingly successful at reducing the length of the chain that connects food production and consumption in an attempt to counteract the impact of the contradictions of the industrial food system and its supermarket-dominated distribution. Their grassroots actions, aimed at overcoming pre-existing socio-economic structures, are in line with social innovations, which have the objective of promoting the social participation of consumers and producers in food systems (empowerment, socio-political activism or social integration in society). In Italy, the Solidarity Purchasing Groups (SPGs), have been the subject of numerous academic studies, but the scope of these studies has, to date, been limited to the political activism of the consumer. The ability of this experience to foster the social participation of the groups' suppliers and the effects that the exchange has on the economic life of the producers have not yet been adequately studied. This article addresses this gap by investigating the extent to which SPGs can reduce the economic marginalisation of their suppliers and evaluates if the activities they promote could increase their social participation. Based on quantitative and qualitative data, this study shows that SPGs, in contrast to other AFNs, maintain a clear separation between consumers and producers and this could mitigate the positive impact of these initiatives on their suppliers. Our analysis of the suppliers shows that SPGs can act as a safety net against economic downturns and that the social participation of the producers involved is higher at the macro, meso and individual levels, compared to producers who do not cooperate with the SPGs.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".