The illusion of inclusion: contextual behavioral science and the Black community
Bibliographic record
Abstract
Anti-racism approaches require an honest examination of cause, impact, and committed action to change, despite discomfort and without experiential avoidance. While contextual behavioral science (CBS) and third wave cognitive-behavioral modalities demonstrate efficacy among samples composed of primarily White individuals, data regarding their efficacy with people of color, and Black Americans in particular, is lacking. It is important to consider the possible effects of racial stress and trauma on Black clients, and to tailor approaches and techniques grounded in CBS accordingly. We describe how CBS has not done enough to address the needs of Black American communities, using Acceptance and Commitment Therapy (ACT) and Functional Analytic Psychotherapy (FAP) as examples. We also provide examples at the level of research representation, organizational practices, and personal experiences to illuminate covert racist policy tools that maintain inequities. Towards eradicating existing racism in the field, we conclude with suggestions for researchers and leadership in professional psychological organizations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".