Racial Equity for Culturally Specific Organizations: An Assets-Based Evidentiary Assessment
Bibliographic record
Abstract
Culturally specific organizations have been missing out on the racial equity and cultural responsiveness initiatives that have been prominent in nonprofits and government, both due to the absence of relevant assessment tools, as well as discourses within these initiatives which have overlooked the sector of culturally specific organizations and subsequently provided a competitive advantage to culturally responsive organizations. This paper reports on the development of a culturally specific organizational assessment, and the results of a four-site pilot study of the tool. The assessment tool is based on the results of a Delphi and Consumer Voice two-part study into the assets of culturally specific organizations and their valuation by clients. Developed with the input of four organizations serving a range of communities of color, we have confirmed that the Successful Families Tool achieves its objectives (centering primarily on creating actionable insights and quality improvement plans). Additional benefits emerged from the pilot’s participants: that an evidence-base for the organization’s assets is initiated, that organization-wide identity is strengthened, that staff have been energized by relevant equity dialogues and relationship deepening, and that motivation for research and evaluation that captures the fullness of the organization’s impacts be undertaken. A sampling of tool questions and standards is included, along with listing of its ten domains.
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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 | 0.032 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".