Through the mud or in the boardroom: Examining activist types and their strategies in targeting firms for social change
Bibliographic record
Abstract
Research summary : We examine the variety of activist groups and their tactics in demanding firms' social change. While extant work does not usually distinguish among activist types or their variety of tactics, we show that different activists (e.g., social movement organizations vs. religious groups and activist investors) rely on dissimilar tactics (e.g., boycotts and protests versus lawsuits and proxy votes). Further, we show how protests and boycotts drag companies “through the mud” with media attention, whereas lawsuits and proxy votes receive relatively little media attention yet may foster investor risk perceptions. This research presents a multifaceted view of activists and their tactics and suggests that this approach in examining activists and their tactics can extend what we know about how and why firms are targeted . Managerial summary : The purpose of this study was to examine how different types of activist groups behave differently when targeting firms for social change. We find that traditional activist groups rely on boycotts and protests, whereas religious groups and activist investors rely more on lawsuits and proxy votes. Additionally, we find that protests and boycotts are associated with greater media attention, whereas lawsuits and proxy votes are associated with investor perceptions of risk . Copyright © 2015 John Wiley & Sons, Ltd.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".