A Lot Can Happen in a Few Minutes: Examining Dynamic Patterns Within an Interaction to Illuminate the Interpersonal Nature of Personality Disorders
Bibliographic record
Abstract
Although problematic interpersonal tendencies have often been characterized as a traitlike excess of a particular interpersonal style, the interpersonal nature of personality disorders may have more to do with patterns of variability in interpersonal behavior and the relation of this variability to the varying behavior of interaction partners. Indeed, problematic interpersonal tendencies may often be evident as patterns within even one interaction. A useful methodology for examining moment-to-moment patterns within the course of an interaction is the computer joystick technique. To illustrate the potential of this new approach for studying problematic interpersonal patterns, the authors provide joystick-based analyses of the videoed session between Dr. Donald Meichenbaum and the client, Richard (Shostrom, 1986a). The authors show how to examine the association between concurrent levels of dominance and affiliation within a person, patterns of covariation between partners, and the moderation of such entrainment patterns. They also discuss how these indices could illuminate disordered interpersonal patterns.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".