“I Can See Some Sadness in Your Eyes”: When Experiential Therapists Notice a Client’s Affectual Display
Bibliographic record
Abstract
We use the methods of conversation analysis to examine how therapists draw attention to a client’s verbal or nonverbal affectual stance display and thus place the focus of talk on the client’s here-and-now experience. These therapist practices are referred to as noticings. By investigating four different experiential-oriented therapeutic approaches (Emotion-Focused, Gestalt, Symbolic Experiential, and Narrative), we explore three ways in which therapist noticings manage the progressivity of talk: by facilitating, shifting, or manipulating/disrupting the activity in progress. We also found that therapists would put noticings to use with varying degrees of empathy and cooperativeness. Whereas empathically designed noticings would facilitate progressivity and cede epistemic authority to clients, nonempathic noticings would disrupt sequential progression and challenge the clients’ greater epistemic status pertaining to their domain of experience. Differences and similarities between therapy approaches with respect to how therapists deploy noticings are discussed. Data are in American English.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".