The deployment of wind turbines: Factors which create accepting attitudes in local communities
Bibliographic record
Abstract
Wind turbines have been a popular choice for renewable energy since the recognition of the environmental and economic threats that have been posed by climate change in the early 20th century (Merkley, 2013). Wind turbines transfer the wind's kinetic energy into mechanical energy. The mechanical energy is then converted into electrical energy, and is transferred to a power grid. Due to the structural design, wind turbines are only efficient in regions of high wind strengths and are primarily deployed in large clear landscapes. Many European countries have displayed a moderate to strong public support for the implementation of wind turbines in their landscapes. Despite the high level of support for this type of technology in principle, many wind turbine development projects in many countries around Europe have been delayed or rejected due to local opposition. Many individuals are concerned with the potential health, environmental, and aesthetic impacts. Local citizens, developing companies and empowered political figures, all have their own understandings of the effects of the existing, as well as the future developments of wind turbines in their countries. The purpose of this paper is to examine what factors create accepting attitudes towards the development of wind turbines in local communities in France and Germany. These factors will then be used to assess a Canadian case to suggest that similar factors are influential in the Canadian context.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".