The Closing-the-Gap Effect: Joint Evaluation Leads Donors to Help Charities Farther from Their Goal
Bibliographic record
Abstract
Charitable donations can be influenced by the level of progress a cause has made toward its fundraising goal. The current work demonstrates how jointly considering more than one charitable cause along with their goal progress information shifts consumers' donation decisions. When charitable causes are evaluated jointly (vs. separately), the comparison makes relative need for help more salient and easier to evaluate, leading to greater giving to the cause farther from its goal. A multimethod investigation, involving six preregistered experimental studies, seven supplemental studies, and a large secondary dataset with over 10,000 projects from a micro-crowdfunding platform, provides evidence for this phenomenon and demonstrates that it is robust to variations in the type of cause, the number of projects, and the donor being able to personally complete the goal. Conversely, the effect is eliminated or reversed when charities are evaluated separately (as relative need for help is less salient), when the gap between charities is smaller (as perceptions of relative need for help are diminished), or when for-profit businesses are evaluated (as the context does not heighten sensitivity to need). This work contributes to research on goal progress and evaluation mode and has implications for charitable giving in comparative contexts like crowdfunding.
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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 | 0.020 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 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".