Negotiating within Whiteness in Cross-Cultural Clinical Encounters
Bibliographic record
Abstract
Despite awareness in social work and related literatures that sociocultural power dynamics are reproduced in practice, there is little research on how whiteness manifests as an oppressive discourse in clinical settings. This article analyzes audio-recorded therapy sessions between white therapists and racialized immigrant clients from an urban community mental health center in Canada to explore the ways in which whiteness shapes clinical encounters. Using poststructural theories of discourse and conversation analysis, the authors examine how discursive strategies that therapists and clients use in therapy sessions produce and reify whiteness as a prominent feature of cross-cultural communication. The findings illustrate how therapists maintain whiteness as an unmarked norm in their assessment of individual development and the family life cycle and how clients respond to, negotiate with, and resist whiteness, which positions them as subordinate others in Canada. The authors conclude with a discussion of implications for practice and future research.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.034 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| 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".