OVERLOOKED: Foundation Support for Native American Leaders and Communities
Bibliographic record
Abstract
Recent years have seen increased attention from philanthropic leaders to questions about race, systemic racism, and systemic inequities. This increased attention was heightened by the ways that the COVID-19 pandemic exacerbated existing inequities and the national protests in the wake of the murder of George Floyd by police. Since early 2020, some foundations have made greater efforts to address systemic inequities by increasing their funding to nonprofits serving communities of color. More than 40 percent of foundations report increasing their funding to nonprofits serving Black communities, and a little more than a quarter report doing so for nonprofits serving Latino communities. However, other communities affected by systemic inequities, including Asian American and Pacific Islander (AAPI) and Native American communities, appear to have been overlooked. These communities have not received much increased support from foundations during the same period. Across four research studies the Center for Effective Philanthropy (CEP) has conducted in the past two years, we've noticed two concerning trends emerge for AAPI and Native American nonprofit leaders and communities (trends that we do not see for nonprofit leaders and communities of other races/ethnicities):1. AAPI and Native American nonprofit leaders report having less positive experiences with their foundation funders than nonprofit leaders of other races/ethnicities. This has been the case during, as well as prior to, the pandemic.2. Despite the significant challenges facing AAPI and Native American people, most foundations continue to overlook nonprofits that serve these communities.We are sharing these results in a two-part series. The first report in the series focuses on findings about AAPI communities and leaders. This second report focuses on findings about Native American communities and leaders. Both reports include stories of nonprofit leaders from these communities, in their own words.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.002 |
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".