Serving three masters: Thematic analyses reveal inherent ethical tensions in fundraising
Bibliographic record
Abstract
Chapman, C. M., Hornsey, M. J., Bleiker, R., & Hutchison, E. (2025). Serving three masters: Thematic analyses reveal inherent ethical tensions in fundraising. Nonprofit and Voluntary Sector Quarterly. Although nonprofit fundraisers face considerable critique about the ethics of their work, research has not typically examined the perspectives of fundraisers themselves. Applying Charitable Triad Theory, we propose that fundraising is inherently fraught with ethical tensions because it involves consideration of three key stakeholders: donors, beneficiaries, and fundraisers. We surveyed 69 professional fundraisers working in diverse nonprofits and asked them how they perceived the ethical landscape of their work. Thematic analyses revealed that fundraisers perceive ethical challenges relating to donors (e.g., soliciting from vulnerable donors), beneficiaries (e.g., how beneficiaries are depicted), and the fundraising organization itself (e.g., how funds are used). A quarter of respondents talked explicitly about the balancing act required to manage competing ethical demands. The triadic lens nuances theorizing on fundraising ethics by highlighting inherent ethical tensions. Findings can inform the development of codes of conduct that engage with the unique, triadic nature of fundraising ethics.
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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 | 0.072 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".