Exploring the need for government energy policy-makers to consider social impacts
Bibliographic record
Abstract
As governments around the world aim to develop and enact policies that promote benefits to the public good, there is an increasing need to identify and acknowledge the social impacts of such policies. In some cases, the social impacts may be unexpected. An example is the social impact related to renewable energy policies, particularly as related to industrial-scale wind power generation. In Ontario, Canada, the push toward large-scale or utility-scale wind power development has resulted in: economic change; social discontent in some affected rural communities; and, concerns about adverse health effects. If the usual avenues of social input to decision-making processes have been removed by legislation, an imposed government policy may result in loss of confidence and, despite the government’s good intentions, may not achieve the intended outcome. While citizens may protest that a policy has inflicted significant social change without consent, some governments may maintain that the overarching goal of environmental benefit outweighs social concerns. This article explores the social impact of wind energy development in Ontario, Canada’s rural communities, and suggests a greater role for social research in informing future policy development.
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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 | 0.025 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".