What do Social Activists Look for? Identifying Configurations of the Corporate Opportunity Structure
Bibliographic record
Abstract
Prior research on the corporate opportunity structure for social activism has yet to consider the possibility that social activists are likely to perceive and evaluate the attractiveness of firms as viable targets holistically–that is, as complex configurations (i.e., prototypes) of characteristics, rather than as lists of independent factors. As such, extant research on the corporate opportunity structure has not addressed why and how configurations of firm characteristics cause some firms to be more highly targeted than others. I seek to develop a comprehensive understanding of configurations of the corporate opportunity structure for social activism; that is, combinations of firm characteristics that make such firms more highly targeted by social activists than others. To do so, I integrate extant theory and research on the key features of corporations that impact the likelihood of a firm being targeted by social activists. I use fuzzy set qualitative comparative analysis (fsQCA) to investigate the combinations of corporations' features that exist among S&P 500 firms that increase the likelihood that such firms will be targeted by social activism. From this analysis, I develop an initial typology of different corporate opportunity structures and thus, offer a mid-range theory of the corporate opportunity structure for 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".