Sustainable Management of Productive Cultural Landscapes: The Pascual Harriague Wineries in Salto as a Case Study
Bibliographic record
Abstract
This article proposes guidelines for the creative management of productive cultural landscapes.These guidelines are briefly illustrated with reference to a case study: the productive cultural landscape of wine and vineyards in the riverside city of Salto, Uruguay, during the last years of the 19th century.The proposed guidelines follow the order and approaches of the links in the Landscape Value Chain.These steps are applied to the landscape from a triple approach, as memory, image and socio-system.Thus, the identification of traces and narratives of memory, elements of image and poles of opportunity of the socio-system is proposed.Each element is valued, considering its potential for re-signification and its cost.An intervention is also proposed, based on reversibility and humility.And, at all times, a process of dissemination or accountability and socialization or social dialogue is maintained.In conclusion, the recovery of a landscape must be understood as something that implies re-signifying its memory (activating its traces with narratives), the restoration of its image (giving it continuity) and restoring its social system (reactivating the socioeconomic dynamics based on the feeling of belonging), through an adequate social participation and a required subjective, non-positivistic approach to the processes, to achieve our objective: the recovery of the character of a productive cultural landscape to encourage the entrepreneurship of its inhabitants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".