Organic Farming and Social-Ecological Resilience: the Alpine Valleys of Sölktäler, Austria
Bibliographic record
Abstract
Farming in the Austrian Alps is small in scale and involves a high degree of manual labor. In the face of structural changes in agriculture, alpine farms are finding it increasingly difficult to remain economically viable. Organic farming presents a promising alternative for alpine farmers because it receives considerable financial support under the Common Agricultural Policy of the European Union. Recent years have seen an increase in the number of organic farms in Austria in general, and in alpine areas in particular. Using data from an empirical study carried out in the alpine area of Sölktäler, Austria, this paper examines the issues of how closely the regulations and principles of organic farming match farmers' perspectives on sustainable agriculture and whether or not organic farming is capable of building social-ecological resilience for local farms. Qualitative interviews and a series of workshops were used to learn about farmers' "desired system state" with regard to their region, disturbances to this system, and their perspectives on organic farming. The desired system in Sölktäler as formulated by the farmers depicts a vivid farming community that manages a diverse traditional agricultural landscape and performs a number of ecological services. The desired system and the principles of organic farming have several aspects in common, and many management practices and features of the social system support social-ecological resilience. The vulnerability of farms increases, however, when farmers must deal with structural changes in agriculture, the erosion of traditional ecological knowledge, and societal transformation. In conclusion, organic farming is a tool that can be used to build social-ecological resilience for Sölktäler farms, because it secures economic funding for the area and makes it possible to sustain environmentally benign practices. What remains is the question of whether the farming community is capable of reorganizing the social system under the pressures of modernization so that the desired system state can be reached.
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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 | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".