Negotiating cultural relevance from within therapy conversations
Bibliographic record
Abstract
Culturally-laden understandings permeate all social interactions, including therapy conversations, and have been referred to as cultural background(s) providing context to the task-at-hand. Historically, cultural backgrounds have been conceptualized as separated from the individual, in pictorial or essentialized ways. Therapists training for multicultural competence tend to focus solely on using a-priori knowledge of clients’ cultural background as a guide to understand clients’ identities. In this article, we show how therapists can explore, understand, and recognize cultural backgrounds from within therapy conversations. We contend that cultural backgrounds are performed or made evident by clients through their use of language, and are evolving dynamic, and socio-historically situated. We provide examples of a session between a therapist, a mother and her two daughters, showing how they coordinate their talking, turn-by-turn, showing what is relevant to them in regards to aspects/structures of their cultural backgrounds that help them deal with the task-at-hand.
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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 | 0.025 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.023 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".