Connecting policy change, experimentation, and entrepreneurs: advancing conceptual and empirical insights
Bibliographic record
Abstract
With global environmental problems worsening, policy makers and nonstate actors are looking for viable solutions through policy innovation, entrepreneurship, and experimentation. Research into the use of experiments to innovate is increasing, but the role of experimentation in policy change has yet to be specifically addressed in the context of climate governance. My aim is to improve understanding by examining how entrepreneurs, key agents of change, might use experiments to advance their climate innovations. Policy entrepreneurs can benefit in several ways from using experiments, including assessing public response to new ideas and learning. I address the question: What role can experiments play in an entrepreneur's change strategies? To answer this, a set of 18 policy experiments from Dutch water management was analyzed to understand how the policy experiments functioned as 4 different policy change strategies. The results revealed that organizers use experiments to evaluate their preformed ideas, to soften local communities to the idea of experimentation, to build broad but centrally controlled coalitions, and to link with influential political actors and national programs to maintain visibility and relevance. These insights formed a list of suggestions that the experiment organizers identified as key to the change strategies. Based on this, a number of recommendations about design choices were made for entrepreneurs who want to experiment. Analyzing experiments as change strategies contributes a novel perspective on how policy experiments function as venues for invention and provides useful suggestions on how experiments can be designed to improve their influence over policy-making processes.
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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 | 0.093 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.007 | 0.074 |
| Scholarly communication | 0.024 | 0.069 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".