A conversation analytic study of building and repairing the alliance in family therapy
Bibliographic record
Abstract
In this paper, we draw from the methods of conversation analysis to illustrate how different alliances that form the ‘web of relationships’ of family therapy are ruptured and subsequently repaired. By focusing on the interactional practices of a master therapist, Dr Salvador Minuchin, we examine how he effectively manages a disaffiliative episode that occurred at the very beginning of a therapy session. In particular, we show how Minuchin's practices function to re‐establish consensus and a positive alliance and to endorse the mother's parental authority (in a context where she claims to be helpless and lack agency). Minuchin thus uses a range of alliance building strategies to join with the family by sharing their distress and, at the same time, moves the conversation forward in a therapeutically‐driven direction to facilitate a restructuring of familial roles and relations. Practitioner points Find ways to disagree with clients’ position without generating stress or rupture in the alliance Verbal and nonverbal discursive moves to join with clients can create an inclusive context for the therapy Discursive strategies can endorse client authority and counter a self‐deprecating client stance
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".