Leading and lagging indicators in fundraising management: A Canadian case study
Bibliographic record
Abstract
Abstract Fundraisers, managers, and boards in the charitable sector are faced with an ongoing concern: how do they produce sustainable, predictable financial returns for their causes while minimizing the cost of fundraising? One way to address this is to improve the measurement of fundraising activities and this study asks how fundraising results should be communicated within organizations to support sustainability. This case study focuses on the fundraising program from one Canadian charity with a large, diversified fundraising program to examine how fundraisers can move beyond simple end‐of‐year financial ratios and implement one managerial technique, leading and lagging indicators, to improve long‐term financial performance. A literature review, internal interviews, and internal document review are used to identify 81 potential leading and lagging indicators that fundraisers can use to develop a suite of indicators that fit their context, activities, and goals and to identify potential challenges with implementing indicators. The role of organizational context and characteristics in selecting an appropriate suite of indicators is also discussed.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".