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Record W6986281000

OVERLOOKED: Foundation Support for Native American Leaders and Communities

2021· report· en· W6986281000 on OpenAlexaboutno aff

Bibliographic record

VenueIssue Lab (Candid) · 2021
Typereport
Languageen
FieldComputer Science
TopicData Analysis with R
Canadian institutionsnot available
Fundersnot available
KeywordsNative americanFoundation (evidence)Quarter (Canadian coin)African americanGovernment (linguistics)PandemicNative American studies
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.003
Scholarly communication0.0090.005
Open science0.0010.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0340.002

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.

Opus teacher head0.053
GPT teacher head0.340
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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