Connecting policy change, experimentation, and entrepreneurs: advancing conceptual and empirical insights
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it