Putting the "Love of Humanity" Back in Corporate Philanthropy: The Case of Health Grants by Corporate Foundations
Bibliographic record
Abstract
Abstract With the growing call for private sector actors to address global challenges, it is necessary to first assess whether regions with the greatest needs are accessing corporate philanthropy. In this paper, we ask whether corporate philanthropy is reaching those with the greatest health-care needs. Drawing on economic geography and corporate homophily, we argue that corporate philanthropy tends to exacerbate health inequality as grants are destined for counties with fewer health problems. We test and find support for this hypothesis using data on health grants made by US corporate foundations and county-level health data. Our results that corporate health grants are less likely to go to counties which have a lower proportion of medical service providers and insured citizens suggest that corporate foundations are unwittingly complicit in worsening the resource gap between small, poor, rural counties and large, wealthy, urban counties. From an ethical perspective, we provide some guidance as to how this may be corrected.
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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 | 0.025 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.060 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 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".