Mission • Money • Merit: Using the Portfolio Approach to Drive Nonprofit Performance
Bibliographic record
Abstract
Nonprofit organizations are continually faced with the challenge of where to allocate their limited funds and other resources across the diverse range of programs that they offer. Rather than examining each program separately, nonprofits should view their activities as a portfolio of programs. Mission, Money, and Merit are the three critical axes for strategic management of a nonprofit's portfolio. The M3 portfolio approach developed here visually presents the size (typically cost) of each program, as well as the relationships among the programs relative to the nonprofit's mission, resource-cost coverage, and performance quality. The portfolio model then measures the center of gravity for the nonprofit on each axis and the overall balance of the organization's activities. By presenting the complexity of any organization visually and colorfully, management can better see and judge what programs may need enhancing, changing, or eliminating. But this is not all the model offers. Through its participatory approach of asking managers to independently rate each of the programs on the three axes, hidden assumptions are illuminated, differences are highlighted, agreements are shared, and learning takes place. The enhanced communication among managers that occurs as a result of this process contributes enormously and directly to the quality of strategic and tactical decision-making by the nonprofit toward greater productivity, effectiveness, sustainability, balance, and success.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".