Corporate Political Activity, Marginality, and Societal Outcomes
Bibliographic record
Abstract
The legitimacy of corporate political activity (CPA) has generally been examined through an ethical lens, where the two sides of the ongoing debate cannot agree as to whether CPA is beneficial or harmful for society as a whole. Drawing on egalitarian arguments and marginality theory, we present a framework of CPA’s effect on society during crisis conditions. Using the COVID-19 pandemic as a natural experiment, we empirically test our framework in the United States, where approximately half of all fifty states allow direct political donations from corporations to politicians and the other half prohibit them. We find support for our predictions. We find that the economies of states in which corporate political donations are legal weathered the pandemic better than states in which they are prohibited. Further, we find that corporate political donations exacerbate the harmful effects of crises on marginalized communities. The positive relationship between marginalized communities and excess deaths during the COVID-19 pandemic was significantly stronger in states where political donations are legal than in states where such donations are not legal.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".