Wind Acceptance Research (WAR) Systematic Review Data
Bibliographic record
Abstract
The number of studies examining social and community acceptance of wind energy in the US and Canada has increased considerably since the late 1980s. The data archived here contributed to a methodological review of this wind acceptance research (WAR) literature, particularly in response to three articles previously published in Energy Research & Social Science. These include a recent synthesis of WAR by Joe Rand and Ben Hoen (2017) recommending better incorporation of WAR results into development practices and comparability of WAR case studies; an investigation by Kathryn Walsh (2020) and colleagues into potential research fatigue in the related field of unconventional oil and gas development research, and finally a call by Benjamin Sovacool and others (2014, 2018) to increase the theoretical depth and reflection in energy social science. Using a systematic review of 111 WAR articles and a follow-up online survey of 40 corresponding authors, we investigated the location of WAR study sites in the US and Canada, the variety and success of different WAR designs and incentives used, the disciplines and theories dominating current WAR, and finally dissemination practices. Our results suggest that research fatigue is unlikely, yet WAR remains concentrated and in locations distant from the highest installed capacity, and focuses on communities and projects that tend to be novel, controversial, or unique to a specific region. We also find that most WAR lacks an underlying theory, and we conclude by recommending greater qualitative analysis of study site selection criteria, greater integration of existing WAR theories, and greater integration of WAR with solar acceptance research. Finally, we urge wind acceptance researchers to ensure and communicate a clear purpose, value and financial benefit to WAR participants and stakeholders and meaningfully consider the broader community contexts examined. This dataset only includes information obtained via the systematic review. It does not, per IRB guidance, include results from the Corresponding Author Survey.
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 | 0.053 | 0.262 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.059 | 0.048 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".