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Record W4386357745 · doi:10.1177/26344041231199908

Negotiating cultural relevance from within therapy conversations

2023· article· en· W4386357745 on OpenAlexaff
Inés Sametband, Giacomo Chiara, Joaquín Gaete‐Silva, Tom Strong

Bibliographic record

VenueHuman Systems Therapy Culture and Attachments · 2023
Typearticle
Languageen
FieldPsychology
TopicCounseling Practices and Supervision
Canadian institutionsUniversity of CalgaryMount Royal University
Fundersnot available
KeywordsSituatedMulticulturalismPsychologyCultural humilityRelevance (law)Cultural competenceCultural diversityNegotiationContext (archaeology)Social psychologySociologyPedagogyAnthropology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.747
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.090
GPT teacher head0.393
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2023
Admission routes1
Has abstractyes

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