Benchmarking Foundation Evaluation Practices
Bibliographic record
Abstract
For foundations, there are lots of questions to reflect on when thinking about which evaluation practices best align with their strategy, culture, and mission. How much should a foundation invest in evaluation? What can they do to ensure that the information they receive from evaluation is useful to them? With whom should they share what they have learned?Considering these numerous questions in light of benchmarking data about what other foundations are doing can be informative and important.Developed in partnership with the Center for Evaluation Innovation (CEI), Benchmarking Foundation Evaluation Practices is the most comprehensive data collection effort to date on evaluation practices at foundations. The report shares data points and infographics on crucial topics related to evaluation at foundations, such as evaluation staffing and structures, investment in evaluation work, and the usefulness of evaluation information.Findings in the report are based on survey responses from individuals who were either the most senior evaluation or program staff at foundations in the U.S. and Canada giving at least $10 million annually, or members of the Evaluation Roundtable, a network of foundation leaders in evaluation convened by CEI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.052 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.019 | 0.036 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.020 |
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".