The role of governance systems and rules in wind energy development: evidence from Minnesota and Texas
Bibliographic record
Abstract
Wind energy presents significant opportunity to provide a series of public goods. Drawing on the ideas of J.Q. Wilson and E. Ostrom, we compare options to overcome the obstacles that stand in the way of deploying wind energy in two US states, Texas and Minnesota. Texas outperformed Minnesota in deploying wind energy technology despite Minnesota's ample wind and other natural advantages. To explain this gap in performance, we argue that Texas outperformed Minnesota because of a more fitting governance system and rules for determining (i) boundaries, (ii) cost and benefit allocation, (iii) conflict resolution, and (iv) rule revision. Our approach sheds an alternative yet overlooked lens upon the topic of wind energy development by focusing on how the concentration of power and authority in the hands of a few dominant public and private elites can lead to the successful deployment of a complex renewable technology under some circumstances.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".