Use of Consultants by U.S. Foundations: Results of a Foundation Center Survey
Bibliographic record
Abstract
This article presents the results of a survey launched in January 2014 by Foundation Center, in collaboration with the National Network of Consultants to Grantmakers, examining use of consultants by community, corporate, and independent foundations whose annual giving totals at least $100,000. The survey asked funders to report whether they used consultants in the past two years and, if so, how frequently and for what purposes; they were also asked to report their level of satisfaction with consultants’ work. Funders that did not engage consultants in the last two years were asked why not. The survey also sought open-ended responses about working with consultants. The survey found widespread use of consultants among foundations. While the results of this study tend to emphasize the benefits – taking advantage of external expertise, allowing staff to stay focused on what they do best, bringing fresh or neutral perspectives to the work – respondents were also clear that working with consultants has its challenges.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".