Changes in institutional and social–ecological system robustness due to the adoption of large‐scale irrigation technology in <scp>Navarre (Spain)</scp>
Bibliographic record
Abstract
Abstract Many regional and national organisations promote the modernisation of agriculture by supporting new technologies to increase their territory's competitiveness in a free‐market context. Such technologies and their associated intensive land management practices are geared towards obtaining higher yields. However, their application also entails changes in water and land management institutions, which could alter interactions among multiple components of the agrarian social–ecological system and potentially weaken the system. Here, we assess how these components and their relations change in a village situated in Navarre (Spain) after the uptake of large‐scale irrigation infrastructure. Specifically, we analyse such changes by comparing how the design principles for robust social–ecological systems manifest before and after the adoption of large‐scale irrigation. Our findings indicate that an unequal distribution of water and land induces some farmers to abandon their agrarian activities. Our case study also shows how irrigation communities have partially lost their autonomy to self‐organise and make agrarian management‐related decisions. We suggest that the adoption of large‐scale irrigation in this region contributes to a decrease in cooperation among resource users, and between users and infrastructure providers. This is due to a decline in the capacity to achieve collective‐choice arrangements and higher external control and monitoring of water use. We argue that the current agrarian management changes may damage social–ecological system robustness and affect the sustainable use of common‐pool resources, leading farmers to maladaptation to climate and market variability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".