Leveraging Effective Consulting to Advance Diversity, Equity, and Inclusion in Philanthropy
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 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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.030 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it