The therapeutic relationship in action: How therapists and clients co-manage relational disaffiliation
Bibliographic record
Abstract
Over the past three decades a great deal of energy has been invested in examining the consequences of relational stresses and their repair. Less work has been done to examine how therapists and clients actually achieve re-affiliation through verbal and non-verbal resources, how such affiliation becomes vulnerable and at risk, and how therapists attempt to re-establish affiliative ties with the client-or fail to do so. We utilize the method of Conversation Analysis (CA) to examine clinical cases that involve extended episodes of disaffiliation. Clients with different styles of disaffiliation-confrontation and withdrawal-are compared. We show how disaffiliation is interactionally realized in different ways and how this is followed by more or less successful attempts at repair.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.005 |
| 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".