Hall of Mirrors: Corporate Philanthropy and Strategic Advocacy
Bibliographic record
Abstract
Abstract Information is central to designing effective policy, and policy makers often rely on competing interests to separate useful from biased information. We show how this logic of virtuous competition can break down, using a new and comprehensive data set on U.S. federal regulatory rulemaking for 2003–2016. For-profit corporations and nonprofit entities are active in the rulemaking process and are arguably expected to provide independent viewpoints. Policy makers, however, may not be fully aware of the financial ties between some firms and nonprofits—grants that are legal and tax-exempt but hard to trace. We document three patterns that suggest that these grants may distort policy. First, we show that shortly after a firm donates to a nonprofit, the nonprofit is more likely to comment on rules on which the firm has also commented. Second, when a firm comments on a rule, the comments by nonprofits that recently received grants from the firm’s foundation are systematically closer in content to the firm’s own comments, relative to comments submitted by other nonprofits. Third, the final rule’s discussion by a regulator is more similar to the firm’s comments on that rule when the firm’s recent grantees also commented on it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 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".