Explaining the Dynamics of Transition to an Integrated WEF System: Two Cases of Irrigated Agriculture in Oregon, USA
Bibliographic record
Abstract
In recent years, the integrated Water-Energy-Food (WEF) Nexus approach has gained traction as a more effective way to manage these interdependent and essential resources. A growing number of traditional food and water irrigation systems in Oregon, USA are transitioning to modernized, holistic, and sustainable Hydro-Irrigation-Restoration Systems that embrace the WEF Nexus approach (Weber 2017). The question is: what factors explain the successful transition? Two cases of irrigation modernization in Hood River, Oregon demonstrate that system transitions follow a pattern of socio-technical change wherein four structural factors are key: economic incentives, changing values expressed in regulations, technological innovation, and external shocks (e.g., major disasters). Yet, while the structural variables associated with the social-technical change approach are necessary for explaining the transitions to new WEF systems in Hood River, they are not sufficient. The case studies display the crucial importance of individual agency, or the actor dynamics capable of enabling or hindering system transformations (see Van Driel and Schot 2005, 54). Chief among these “agency” factors are (1) facilitative, visionary, trust-worthy leadership, (2) the cultivation of trust and collaborative problem-solving capacity, (3) the willingness to embrace risk and trade short-term costs for the potential of long term gains (e.g., low discount rates), and (4) the adoption of a new set of ideas, or shared norms, governing decision-making which embraced the idea that an integrated, modernized system could simultaneously promote economic, environmental, and energy sustainability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".