Fundraising Effectiveness Project (FEP) Second Quarter Fundraising Report (2022)
Bibliographic record
Abstract
Charitable giving increased significantly in Q2 2022, but gains were accompanied by a continuing steep decline in donor acquisition and retention, particularly among new and newly retained donors, according to the Fundraising Effectiveness Project's (FEP) Second Quarter Fundraising Report.The Fundraising Effectiveness Project (FEP) is a collaboration among fundraising data providers, researchers, analysts, associations, and consultants to empower the sector to track and evaluate trends in giving. The project offers one of the only views of the current year's fundraising data in aggregate to provide the most recent trends for guiding nonprofit fundraising and donor engagement. The FEP releases quarterly findings on those giving trends, released both via downloadable reports at afpfep.org and in a free online dashboard. FEP Q2 2022 Report Key TakeawaysQ2 giving data shows donor counts down steeply, driven by declines in small (sub $500) donor segments, as well as in new donor acquisition and retention. On the other hand, recaptured donors and newly retained donors, which had both dropped in Q1, rose moderately and stabilized in Q2.At the same time, dollars are up, largely due to increased giving by major donors, although this increase of 6.2% (estimated for late data) is nonetheless lower than the Q2 inflation rate of approximately 8.5%Despite decreasing overall donor counts, fundraising is up thanks to increasing recapture rates (people who donated sometime in the past, but not last year). That segment may include COVID donors being recaptured or the return of pre-COVID donors who paused their giving during the pandemic.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.095 | 0.041 |
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".