Negotiating with Environmental Activists in the Shadow of Regulation
Bibliographic record
Abstract
We study interactions between firms and environmental activists that occur in the regulatory domain to examine the conditions under which they reach cooperative outcomes to pre-empt formal regulatory contestation. We argue that a munificent regulatory environment plays a central role in enabling cooperative outcomes by providing firms greater access to resources and regulatory flexibility that allows them greater discretion in addressing the demands of environmental activists. We further contend that regulatory munificence mitigates the effects of firms’ environmental impact, activists’ inclination to adopt contentious tactics, and pressures from competing stakeholders – attributes that hinder the ability of firms and environmental activists to achieve cooperative outcomes. We empirically test our predictions in the context of U.S. electric utility sector, where state-level regulatory proceedings provide stakeholders, including environmental activists, the opportunity to participate and influence regulatory outcomes. Statistical analysis of the outcomes of negotiations between firms and environmental activists in these state regulatory proceedings between 1990 to 2015 provides strong support for our predictions. Our study contributes novel insights to the research on firm-stakeholder relations by examining interactions that occur between them in the relatively understudied, but critical, regulatory domain.
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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".