VolunteerMatch.org: balancing mission and earned‐revenue potential
Bibliographic record
Abstract
Purpose This case investigates how a nonprofit can analyze its earned revenue potential. What changes would be required for the organization's current business units to start making a positive financial contribution? What other opportunities to expand its earned‐income efforts exist, and how should they be prioritized? What would it take to implement the new ventures, and how could the nonprofit guard against undertaking initiatives that would subtract more from the organization – in dollars and staff time – than they could possibly add? Design/methodology/approach A team of consultants from Bridgespan worked with VolunteerMatch, the largest web‐based volunteer‐matching service in the country, to study how to make its earned revenue ventures generate income for the organization and support its mission. Findings VolunteerMatch's work on earned income helped it to move forward with its financial goals, and also to strengthen its social mission. Research limitations/implications VolunteerMatch is a small, talent rich nonprofit with a staff that is comfortable innovating internet‐based products and services. Expanding the study to include a variety of nonprofits would provide a better indication of the viability of an earned income strategy in this sector. Practical implications VolunteerMatch now derives 38 percent of its revenue from its earned income activities, decreasing its reliance on contributions. Originality/value Few detailed studies exist of the development of earned income operations in nonprofits. This one serves as a guide to best practices for organizations considering this strategy.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".