Rethinking Services with Communities of Color: Why Culturally Specific Organizations Are the Preferred Service Delivery Model
Bibliographic record
Abstract
Racial disparities in social, education and health services continue unabated despite efforts to address them. At the margins of the service delivery system are lesser-known and minimally researched programs known as “culturally specific organizations” that have been developed by and with communities of color. These are organizations that have been developed by a specific community of color and continue to serve that same community of color. This article shares the insights of three leaders in racial equity, who have been immersed in Portland-based organizations for many years: two as organizational leaders and one as an academic research partner. The paper details the organizational assets, the research that provides emerging evidence of their contributions, and the resistance faced by its advocates. Additionally, original qualitative research contributes to this article: insights of the lived experience of leaders of color, and notes gathered over the years of presentations and dialogues in the region have been analyzed. Three additional assets are identified, adding to the seven assets that emerged in the literature. The article closes by identifying the implications that such organizations hold for education, research and practice.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.007 |
| 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".