Social relationships and intraindividual variability in interpersonal behavior: Correlates of interpersonal spin.
Bibliographic record
Abstract
Personality constructs are typically conceptualized as central tendencies of the individual. We explore whether dynamic personality constructs that quantify the within-individual variability of behavior across situations and over time predict the closeness of social relationships. We focused on interpersonal spin, defined as the degree of dispersion in a person's interpersonal behaviors around the interpersonal circumplex across situations and over time. We predicted that individuals with high spin would have social relationships that are less close than individuals with low spin. In 3 studies with different measures of relationship closeness, we found that (a) higher spinners reported that a larger proportion of their contacts in their workplace social networks were distant (Study 1); (b) co-workers were less satisfied and less often engaged in pleasant activities with higher spinners (Study 2); and (c) co-workers avoided higher spinners with whom they were well acquainted (Study 3). Moderated mediation analyses in Study 3 revealed that co-workers avoided well-acquainted higher spinners because they felt more negative affect when interacting with these individuals. The findings suggest the potential of dynamic personality constructs for improving our understanding of the characteristics of individuals' social relationships.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".