Shareholder Activism from a System’s View: How the Media and Regulators can Facilitate the Campaigns
Bibliographic record
Abstract
Amid extensive research on the direct effects of shareholder activist demands on target firms, recent calls have been made to understand the broader system in which activism unfolds, including the actors that facilitate or hinder activists’ efforts. In this study, we develop a theoretical framework that positions the outcomes of shareholder activism as contingent on the actions of other third-party stakeholders. The fulcrum of our theory is that activist demands will more effectively motivate organizational change when other stakeholders reinforce the issues raised by shareholder activists. Situated in the context of shareholder proposals, we theorize and find that the potential for proposal-based activism to enact organizational change hinges on the scrutiny of proposed issues raised by the media and regulators, which, respectively, bring coverage and credibility to the issues at hand. This study steps beyond the activist-firm dyad to provide new insights into the role that non-shareholding stakeholders play in shareholder activism, and raises new questions about stakeholder 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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.020 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".