Keeping up with the Joneses: The relationship of perceived descriptive social norms, social information, and charitable giving. Nonprofit Management and Leadership19
Bibliographic record
Abstract
We study the influence of perceived descriptive social norms on subsequent giving behavior to nonprofits, explore how social information can influence these norms, and provide insight for fundraising practice. A survey conducted in a nonprofit organi-zation first shows that donors use their beliefs about the descrip-tive social norm to inform their own donation behavior. Donors who believe that others make high contributions tend to make high contributions themselves. Next, a laboratory experiment demonstrates the influence of social information on the descrip-tive social norm and consequently on giving. These results sug-gest strategies for fundraising practice. Informing donors of contributions made by another person influences their percep-tions about the descriptive social norm, which in turn influences their giving behavior. We conclude with a discussion of theoret-ical and practical implications. IN 2005, ALMOST $200 BILLION WAS RAISED by U.S. nonprofit organi-zations from individuals and households (Giving USA Foundation,2006). A significant portion of these individual contributions ($36.92 billion) was from nonitemizing individuals, who contribute on average about $551 a year (Giving USA Foundation, 2006). This is not a phenomenon limited to the United States. In Canada, individual donations totaled $8.9 billion in 2004, with donors giving an average of $400 each (Hall, Lasby, Gumulka, and Tryon, 2005). In the United Kingdom, individual donations were £8.9 billion in 2005–2006
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".