I think you might like me: Emergence and change of meta-liking in initial social interactions.
Bibliographic record
Abstract
= 15.48, 61.10% female) have default expectations for meta-liking at zero acquaintance and how these judgments are updated during initial group interactions. Specifically, we used latent change models to examine how personality traits predicted initial meta-liking and whether personality and social interaction experiences were linked to changes in meta-liking judgments throughout an interaction. Our findings revealed three key insights: First, meta-liking increased gradually over the course of the interaction, with substantial individual differences in both default meta-liking and change scores. Second, extraversion, neuroticism, and self-esteem predicted initial meta-liking. Third, liking others was also linked to initial meta-liking and early changes, while meta-liking changes toward the end of the interaction occurred independent of all these features and were not predicted by expressive behaviors of interaction partners. This study represents a first empirical test of default expectations and updates in meta-liking based on personality characteristics and social interaction experiences in initial social interactions. We discuss our results in terms of a broader framework for understanding how metaperceptions are formed and updated early in the acquaintance process. (PsycInfo Database Record (c) 2024 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".