Clinical practice with people of color : a guide to becoming culturally competent
Bibliographic record
Abstract
Chapter 1: The APA Multicultural Guidelines on Education, Training, Research, Practice, and Organizational Change: A Brief Overview Madonna G. Constantine, Christina M. Capodilupo, & Mai M. Kindaichi Chapter 2: Asian American Populations Bryan S. K. Kim Chapter 3: African American Populations Juanita Martin Chapter 4: Latina/o American Populations Cynthia de las Fuentes Chapter 5: American Indians and Alaskan Native Populations John J. Peregoy & Alberta M. Gloria Chapter 6: Arab American Populations Sylvia C. Nassar-McMillan Chapter 7: Biracial Populations Angels R. Gillem, Sean Kathleen Lincoln, & Kristen English Chapter 8: Immigrant and Refugee Populations Rita Chung & Fred Bemak Chapter 9: Lesbian, Gay, and Bisexual People of Color.
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 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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".