Bursting the bubble? The hidden costs and visible conflicts behind the Prosecco wine ‘miracle’
Bibliographic record
Abstract
Prosecco, a wine that two decades ago was virtually unknown outside of Italy and was considered inferior to other sparkling wines, has become immensely popular. But how did Prosecco producers gear up to meet a booming demand in a highly regulated wine industry such as Italy's? Is this an example of an inclusive growth trajectory? Who is capturing the benefits of this growth and who is bearing its hidden costs? Through the case study of Prosecco, I identify the everyday practices and struggles that underpin the growth of Prosecco in relation to nature, landscape and land use, and examine how the environmental, health and other hidden costs of agro-food value chains shape various layers of visible conflict. The great growth that has characterized the ‘Prosecco miracle’ of the 2010s arises from the reinvention of a geographic origin that was under threat following the 2008 EU wine reform. The ‘discovery’ of a village named Prosecco, located quite far from the original core area of Prosecco production, provided the vector for a large expansion of Prosecco viticulture and wine production, and the emergence of a veritable export bubble. This expansion, supported by key institutions, regulators and the regional political elite, is putting pressure on nature and landscapes and is fomenting local protests against indiscriminate agro-chemical spraying. I find that, while the industry claims to be addressing its key sustainability challenges, a number of conflicts and tensions persist. Ultimately, the case study of Prosecco provides key insights to current debates on the hidden costs of agro-food value chains and their resulting conflicts – confirming that commodity expansion is often linked to processes of appropriation of nature, landscapes and territories, and to the ability of business to capture surplus while externalizing the hidden social, health and environmental costs of production.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".