Benchmarking Foundation Evaluation Practices 2020
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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?
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.107 |
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 it