Bibliographic record
Abstract
A fundamental theoretical premise in Structural Family Therapy (SFT) is that changes in individual members and improvements in intra-familial relations are realized by repairing the family structure. Dysfunctional families are conceptualized in terms of individuals taking on inappropriate roles (e.g., children acting as if they were parents) and the boundaries between parental executive levels and the children/sibling level are unclear, too rigid, or highly permeable. The therapist's role is to temporarily engage (join) with family members in a way that generates in-session interactions that exemplify the desirable family structure. While the theory supporting these interventions are well developed, there has been little work done on explicating how such tasks may be interactively accomplished in clinical practice. Drawing from the methods of conversation analysis, our aim for this paper is to show how a master therapist in SFT accomplishes some of these transformations during a single therapy session. We focus on the discursive resources through which the therapist is able to readjust the role relationships between a mother and her daughter (i.e., in such a way that the mother can adopt a more agentive position vis-a-vis her children) and how the therapist's actions indexed core SFT principles of restructuring the family.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".