Joint Fundraising Appeals: Allocation Rules and Conditions That Encourage Aid Agencies' Collaboration
Bibliographic record
Abstract
ABSTRACT Collaborative fundraising mechanisms—known as joint fundraising appeals—have received increasing attention as innovative ways to reduce fundraising competition between aid agencies. While some aid agencies participate in joint fundraising activities, others consider that the disadvantages outweigh the benefits. Based on the analysis of two‐stage sequential game theory models, the article identifies key factors that affect aid agencies' decisions about participation in a joint fundraising mode. Our analysis indicates that expected effects of the joint fundraising mode depend on aid agencies' efficiency levels. For efficient aid agencies, the joint mode functions as a buffer to prevent them from fierce competition. In the case of inefficient aid agencies, the competitive mode saves aid agencies from underinvestment in fundraising activities. We classify representative allocation rules currently applied in the humanitarian context into three types. Our analysis shows that these allocation rules only lead to beneficial allocations for limited circumstances. Following the analysis of the three representative allocation rules, the present work also analyzes aid agencies' negotiation‐based allocation without predetermined allocation rules, using generalized Nash bargaining.
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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 | 0.017 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".