Hall of Mirrors: Corporate Philanthropy and Strategic Advocacy
Bibliographic record
Abstract
Politicians and regulators rely on feedback from the public when setting policies.For-profit corporations and non-pro t entities are active in this process and are arguably expected to provide independent viewpoints.Policymakers (and the public at large), however, may be unaware of the financial ties between some firms and non-profits -ties that are legal and tax-exempt, but difficult to trace.We identify these ties using IRS forms submitted by the charitable arms of large U.S. corporations, which list all grants awarded to non-pro fits.We document three patterns in a comprehensive sample of public commentary made by firms and non-profits within U.S. federal rulemaking between 2003 and 2015.First, we show that, shortly after a firm donates to a nonprofit, the grantee is more likely to comment on rules for which the firm has also provided a comment.Second, when a firm comments on a rule, the comments by non-profits that recently received grants from the firm's foundation are systematically closer in content similarity to the firm's own comments than to those submitted by other non-profits commenting on that rule.This content similarity does not result from similarly-worded comments that express divergent sentiment.Third, when a firm comments on a new rule, the discussion of the final rule is more similar to the firm's comments when the firm's recent grantees also comment on that rule.These patterns, taken together, suggest that corporations strategically deploy charitable grants to induce non-pro fit grantees to make comments that favor their benefactors, and that this translates into regulatory discussion that is closer to the firm's own comments.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".