Uncertainty confounds justice: the offshore wind ‘Devil they don't know’ in the Northeast United States
Bibliographic record
Abstract
Renewable energy deployment has faced setbacks in part due to political pushback and social opposition. Recent research has focused on the justice dimensions of renewable energy, including fair participatory processes and the equitable distribution of benefits and burdens. Our study broadens this justice lens by focusing on how uncertainty affects perceptions of justice and risk, using offshore wind as a case study. We conducted 37 semi-structured interviews with community members, government officials, fishers, and waterfront workers in the Northeast United States. Our thematic analysis revealed eight themes, which are analyzed within the context of two social science frameworks: energy justice and the social amplification of risk framework (SARF). We found that decision-making processes and information dissemination mechanisms exacerbated participants’ uncertainty about offshore wind, leading to the amplification of risks and the amplification and attenuation of benefits, and perceptions of procedural and distributive injustice. Our discussion outlines a novel framework that integrates SARF and energy justice and explores the implications of uncertainty confounding justice, including suggestions for offshore wind communication and engagement, as well as a call for social science research to give heightened attention to the relationship between risk, uncertainty, and justice.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".