Pilot Projects in Water Management
Bibliographic record
Abstract
Pilot projects appear in many forms in policy making and management.In an effort to understand the nature and use of pilot projects and improve their effectiveness, we undertake a practicebased and theoretical study of the pilot project phenomenon.First, we examine the roles assigned to pilot projects in the policy development literature and explore their use in a Dutch water innovation platform.Second, we determine characteristics of pilot projects to deepen insights into the nature of the pilot project phenomenon and the dimensions useful in the design of pilot projects.Third, we identify three pilot types and nine ways to use a pilot project and we develop a Pilot Project Nonagon that can be used to assess pilot projects' uses and to compare stakeholders' perspectives on these uses.Fourth, we identify hurdles to diffusion of the knowledge developed from pilot projects and suggest strategies to overcome these.Lastly, we formulate a research agenda aimed at addressing the identified knowledge gaps.
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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 | 0.053 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".