Benchmarking Foundation Evaluation Practices 2020
Bibliographic record
Abstract
In 2019, the Center for Evaluation Innovation administered a benchmarking survey to collect data on evaluation and learning practices at foundations. This is an ongoing effort (previous surveys were conducted in 2015, 2012, and 2009) to understand evaluation functions and staff roles; the level of investment in and support of evaluation; the specific evaluative activities foundations engage in; the evaluative challenges foundations experience; and the use of evaluation information once it is collected.The survey was sent to 354 independent and community foundations in the US and Canada reporting at least $10M in annual giving during the previous fiscal year, and to foundations that participate in the Evaluation Roundtable network (the vast majority of which meet the annual giving criterion). This report includes survey data from 161 foundations, a 45% response rate.We conduct the survey so that foundations can compare their evaluation and learning structures and practices to those of the broader sector. The results offer a point-in-time assessment of sector practice. They do not necessarily represent "best" or even "good" practice. They do, however, offer valuable inputs on key questions, such as: How should the evaluation and learning function be staffed and resourced? What kinds of evaluative activities should be prioritized? How can evaluation and learning link to 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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.021 |
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".