Enactments of racial microaggression in everyday therapeutic encounters
Bibliographic record
Abstract
Ruptures, including racial microaggressions, are inevitable in therapy. Because they are subtle and subject to alternative explanations, identifying and illustrating racial microaggressions have been challenging. To critically reflect on such ruptures and ultimately repair the alliance with clients, scholars urge the significance of studying “how” racial microaggressions emerge and become visible in psychotherapy. Drawn from transcripts of actual therapy sessions, we select segments where racially and culturally relevant conversation occurred. Using critical discourse analysis, we explore how clients and therapists reify or resist contested values, norms, and power in therapy by using various discursive tactics in therapy conversation. Our findings illustrate different forms of racial microaggression, how they are managed in moment-to-moment interactions, and how they are associated with dominant values and norms that shape the therapists’ treatment selections in cross-racial encounters. These microdetailed illustrations help therapists to critically reflect on their own behaviors, increase their sensitivity to cultural narratives in therapy communications, and suggest how to better hear and validate cumulative injuries of racial microaggressions that many racialized minority clients often face in psychotherapy.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".