From Revolution to Revenue Stream: How Corporate Targets Co-opt Social Movement Attacks
Bibliographic record
Abstract
Research on social movements has shown that activist attacks on corporate targets can help to create new market opportunities. Because these opportunities tend to be oppositional to incumbent industries, theory posits that incumbents are unlikely to exploit these opportunities. However, we suggest that corporate targets might be able to leverage activist attacks to their own advantage. Drawing on a longitudinal study of commercial academic publishers’ responses to the Open Access Movement, we propose a theoretical model of how incumbent organizations can benefit from the market opportunities resulting from social movement attacks by manipulating powerful third-party stakeholders’ perception of alignment or misalignment with the corporate targets and social movement respectively. To do so, corporate targets first co-opt social movements’ frames by exploiting the distance between activists’ and powerful stakeholders’ concerns. Second, corporate targets redefine social movements’ claims to create new market opportunities that is aligned the powerful stakeholders’ concerns. Our paper moves beyond the current focus on how social movements create new, oppositional markets to how corporate targets co-opt social movement attacks to enhance their market position.
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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 | 0.004 | 0.020 |
| 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 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".