Bibliographic record
Abstract
Chapter 3 introduced a three-step framework that could be applied to case study analysis in order to extract insights for refining wind power development policy. The first step of the framework entailed the analysis of a sufficient number of national case studies to identify prominent commonalities that influence wind power development. In this book Germany, Denmark, China, the United States, Japan, and Canada were chosen as nations for analysis. Germany and Denmark—two nations that have laudable and sustained successes in wind power development—were selected in order to provide insight into successful wind power development policies. China and the United States, which have both experienced boom and bust periods of wind power development, were picked to provide insight into factors that cause such oscillations in development. Japan and Canada, which are two nations that have underperformed in regard to wind power development, were selected to provide insight into barriers to wind power diffusion. Although only six nations were included in this study, additional wind power policy analysis undertaken by the author in Australia and Taiwan provide general confirmation of the external validity of the findings that will be summarized in this chapter. In this chapter the social, technological, economic, and political (STEP) factors that emerged as influential for either supporting or impeding wind power development in the six case study nations will be summarized. The intention of this compendium is to provide policymakers and interested stakeholders with greater clarity regarding the factors that must be strategically managed in order to enhance the scale scope and pace of wind power diffusion. The factors introduced in this chapter should not be misconstrued as constituting a best practice list for optimizing wind power policy success. As was pointed out in the introductory chapter, energy policy is designed and implemented within a contextually unique environment that involves a seamless web of dynamically evolving forces. Consequently, the notion that it might be feasible to construct a universally applicable manual of best policy practice is a fool’s errand.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".