Immigrant family members negotiating preferred cultural identities in family therapy conversations: a discursive analysis
Bibliographic record
Abstract
Abstract In this article we present a discursive analysis of how immigrant family members relationally recognize and co‐articulate with each other's preferred cultural memberships during family therapy conversations. This article draws from a qualitative study of family therapy conversations with a sample of sixteen video‐recorded sessions with nine immigrant families and their therapists, and from separate interviews with each family member. Selected segments of therapy conversations and subsequent individual interviews were transcribed verbatim for the analysis. We show exemplars of how therapists help immigrant family members move beyond dis‐preferred cultural membership ascriptions (i.e. misrecognition) by foregrounding cultural identities family members deem more appropriate. We conclude by discussing how this preference‐animated research can be useful for practitioners to help immigrant family members co‐construct cultural identities that suit them better as individuals and members of a family. Practitioner points Misrecognition occurs when immigrant family members’ preferred cultural identities are disregarded or not acknowledged in family conversations and interactions By foregrounding cultural identities at play in family therapy, practitioners can facilitate dialogues helping family members recognize preferred cultural identities Discursive research methods may enhance therapists’ awareness of how discursive negotiations of cultural identities influence family members’ relationships
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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 | 0.014 | 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 | 0.014 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.007 |
| 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".