Bibliographic record
Abstract
This paper presents research analyzing the role of computational technology in the domain of nonprofit fundraising. Nonprofits are a cornerstone of many societies and are especially prominent in the United States, where $295 billion, or slightly more than 2% of the U.S. Gross Domestic Product (i.e. total national revenue), was directed toward charitable causes in 2006. Nonprofits afford many worthwhile endeavors, including crisis relief, basic services to those in need, public education and the arts, and preservation of the natural environment. In this paper, we identify six roles that computational technology plays in support of nonprofit fundraising and present two models characterizing technology use in this domain: (1) a cycle of technology-assisted fundraising and (2) a model of relationships among stakeholders in technology-assisted fundraising. Finally, we identify challenges and research opportunities for collaborative computing in the unique and exciting nonprofit fundraising domain.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.069 | 0.015 |
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".