Why do organizations take political stances? A review of reasons and risks
Bibliographic record
Abstract
Abstract Organizations and their leaders have begun publicly signaling political values in candidate endorsements, statements, and advertisements, yet political action often has negative organizational consequences, including lower public support, financial costs, and reduced trust. We review the costs of organizational politicization, moderators of those costs (such as ideological alignment and size of the organization), and potential reasons why leaders take political action. Scholars often attribute political action to public pressure to “take a stand”, but this public pressure may be misunderstood. Members of the public who want organizations to take political stances desire particular stances to be made in particular ways, tend to believe in the superiority of their own values, and are relatively likely to boycott businesses for political reasons. Catering to these individuals could lead to the accumulation of supporters who are especially politically zealous and likely to punish perceived political missteps. Demands to “take a stand” might seem like one unified call to action, but they may instead be a large set of directly conflicting demands. We make recommendations for future research to better understand leaders' reasons for political action and when, if ever, such actions support the interests of organizations and broader society.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".