Strategies of Sociopolitical Activism Inside the Firm
Bibliographic record
Abstract
This symposium seeks to understand the strategies related to sociopolitical activism inside of firms as well as the consequences of that activism. In recent years, organizational leaders have publicly expressed positions on social and political matters not directly related to the firm’s primary business activities (Chatterji & Toffel, 2019), while employees have raised social, political, and moral concerns at work and protested a myriad of their organization’s practices and policies (e.g., Briscoe & Gupta, 2021; Davis & Kim, 2021). For this symposium, we curated four papers that contribute to a deeper understanding of the phenomenon of sociopolitical activism inside firms. These papers provide new insights into how employee activists use contentious activism to target their own firms and how they react to leadership responses to this activism, how organizational leaders frame public stances on sociopolitical topics, and stakeholder reactions to such activism. The papers selected for this symposium shed new light on the complexity of how employees protest their own firms as well as the tradeoffs firm leaders experience when deciding how to express positions on sociopolitical matters. Making Movements: How Employee Activists Engage in Contentious Activism Author: Raquel Renee Kessinger; Boston College Threading the Needle: How Firms Frame their Stances on Polarizing Social Issues Author: Kate Odziemkowska; U. of Toronto, Rotman School of Management Media Narratives in Response to Sociopolitical Activism Author: Genevive Gregorich; Columbia Business School To Speak or Not To Speak? Corporate America and George Floyd Author: Olga Hawn; U. of North Carolina, Chapel Hill
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".