The interpersonal nature of self-talk: Variations across individuals and occasions.
Bibliographic record
Abstract
This research addressed the hypothesis that self-talk conveys a variety of interpersonal styles, representing different ways of addressing oneself, and that these different styles can be conceptualized with the interpersonal circumplex. Using a diary-like method for 14 days, 232 undergraduates were asked, toward the end of each day, to reflect on one positive and one negative event from that day and write down their self-talk about each. Using an instrument based on the interpersonal circumplex, participants and later independent judges rated the interpersonal qualities of each self-talk statement. Ratings showed a reasonably high degree of consistency over days, and averages over days for each participant showed reasonably good circumplex structure, consistent with an interpersonal conception of self-talk style. Multilevel modeling revealed that the interpersonal qualities of self-talk also showed substantial variance at the occasion level. There were synergistic interactions of self-talk dominance and affiliation in the prediction of post-self-talk affect, with higher positive and lower negative affect following self-talk that was relatively high in both dominance and affiliation. Conceptualizing self-talk styles in terms of the interpersonal circumplex offers a promising framework for further research on inner experience and its effects. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".