Bibliographic record
Abstract
Chapter 10 summarized nine social factors, seven technological factors, seven economic factors, and nine political factors that have influenced the fortunes of wind power development in the six case study nations covered in this book. The premise underpinning the previous chapter is that successful wind power development policy depends on strategic management of forces of change within four contextual areas depicted in Figure 11.1. There are three basic tenets underpinning this model. First, the environment in which wind power policy is formulated and implemented can be better understood by comprehensive analysis of conditions within four contextual areas: the sociocultural context, the economic context, the technological context, and the political context. Within each of these four areas there are dominant forces (variables) that have proven to be influential in hindering or helping wind power development. The trouble is that for each nation, the relative importance of each influential variable differs because energy policy in each nation is influenced by a unique conflation of sociocultural, technological, economic, and political conditions. For example, a high degree of information asymmetry is evident in both Japan and China. Citizens of both nations lack adequate information about the pros and cons of energy technologies to make informed decisions. In Japan, information asymmetry helps explain why there is so little support for wind power and why the government has been able to continue its advocacy of nuclear power. In China citizens are also kept largely in the dark about energy sector developments, but this is not a problem for wind power development because the government is committed to supporting wind power whether the public consents or not. In short, information asymmetry is a barrier to wind power development in Japan, but in China, it is not. Second, the analysis of STEP forces is complicated because variables within each of these four contextual areas interact in unpredictable ways due to the complexity of variable interrelations. Cause-and-effect links are extensive which means that numerous positive and negative feedbacks catalyze chaotic systemic evolution. For example Canada possesses a wealth of hydropower capacity that suggests a high degree of grid resilience.
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.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.103 | 0.026 |
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".