Motivations Behind Donor Funding Refusal: Towards a Typology of Principled Refusal
Bibliographic record
Abstract
NGOs are perceived as organisations that are always seeking funding. However, there are many instances where donations are refused by NGOs. This counter-intuitive decision, given the often grave humanitarian needs, is not well documented beyond brief references or individual cases. Refusal is an expression of values and principles, important for actors that are often portrayed as having little to no agency or power in relation to donors. We developed a database of 32 examples of funding refusals by NGOs detailing the reasons for refusal. To classify and compare the refusals, we developed a preliminary typology of NGO motivations for donor refusal, which contains four types (independence, impartiality, neutrality, and humanity) that align with humanitarian principles. Each category and type are defined and examples of each are provided. Given the focal nature of NGOs in development activity, the lack of attention to funding refusal is notable. We address this lacuna by creating a database and developing a preliminary typology to provide a foundation for future research. This study contributes a novel typology to an under-studied topic. In so doing, this paper provides a foundation for studies of refusal to follow.
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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 | 0.034 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".