Leveraging Effective Consulting to Advance Diversity, Equity, and Inclusion in Philanthropy
Bibliographic record
Abstract
In 2018, the National Network of Consultants to Grantmakers launched an initiative to sharpen the impact of diversity, equity, and inclusion (DEI) work in grantmaking by increasing the capacity of consultants and grantmakers engaged in these efforts. Network researchers used a systematic protocol to interview consultant members about their most effective partnerships with grantmakers. Case studies drawn from those interviews yielded valuable lessons for advancing DEI in philanthropy. In sharing some of these lessons, this article advises consultants to be prepared to help grantmakers define or refine the meaning of DEI and understand where equity fits into their values and mission. It also explores how a good DEI consulting process helps to distinguish technical and complex dimensions of a DEI commitment, and how the scope of work should encompass both development of internal leadership skills and investment in grantee, community, and issue leaders. This article concludes with tips on how smart DEI consultant/grantmaker partnerships can understand and honor emergent strategy and help the funder follow opportunities without overwhelming the size and scale of the funder’s capacity.
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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 | 0.063 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".