Activist Protest Spillovers into the Regulatory Domain: Theory and Evidence from the U.S. Nuclear Power Generation Industry
Bibliographic record
Abstract
We examine how social activism—in the form of public protests against contentious business practices—can spill over into the regulatory domain, extending beyond activists’ articulated goals to affect firms’ regulatory outcomes in areas that are not directly targeted. We argue that firms are likely to experience broader regulatory repercussions after activist protests because public contention invites greater scrutiny of firm behavior by industry regulators, increasing the likelihood that instances of organizational noncompliance will be discovered. Protests can also cause regulators to evaluate targeted firms more negatively in regulatory assessments, especially firms with less favorable preexisting reputations or stakeholder relations, and to tighten regulations on nontargeted issues that signal their commitment to safeguarding the public interest. We further contend that the political context within which regulatory agencies operate shapes the extent of protest spillovers: When political institutions are aligned with activist goals, and when regulators are ideologically sympathetic too, protests have a more pronounced negative impact on firms’ regulatory outcomes in nontargeted domains. We find robust support for our predictions in a statistical analysis of the impact of antinuclear protests—which sought to block nuclear power plant development by electric utilities—on utilities’ subsequent regulated financial rates of return on their assets. Our analysis contributes new insights to research on the indirect consequences for targeted organizations of social activism.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".