Can shareholder advocacy shape energy governance? The case of the US antifracking movement
Bibliographic record
Abstract
Research on socially responsible investing (SRI) and investor-led governance, especially in the climate sector, suggests that shareholders adopt social movement tactics to influence corporate governance, including building networks, engaging directly with corporations and lobbying regulators. Further, research on corporate transparency and financial disclosure has proliferated, notably in the extractives sector. Our work builds on these existing literatures, with a focus on shareholder resolutions on hydraulic fracturing (HF) in the United States. We analyze US HF-focused shareholder resolutions from 2010 to 2016 to evaluate filing strategies and outcomes. We argue that these resolutions provide space for a range of new actors to shape corporate governance—but their power is constrained. The constraints flow from the same political economy factors that enable shareholders to take collective action: the distance between individual investors and financial decisions; the structure of resolutions and managerial responses; and the complexity of investment vehicles and vote shares. We assess how shareholders respond strategically by altering the focus of resolution demands, liaising with external campaigns and networks, and engaging with government to enhance regulatory interventions. Our work reveals how the upstreaming of power in commodity chains intersects with the power of management boards and the challenges of financialization, with consequences for corporate and energy governance.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".