In person, online, and up close: the cross‐contextual consistency of expressive accuracy
Bibliographic record
Abstract
People vary widely in their expressive accuracy, the tendency to be viewed in line with one’s unique traits. It is unclear, however, whether expressive accuracy is a stable individual difference that transcends social contexts or a more piecemeal, context‐specific characteristic. The current research therefore examined the consistency of expressive accuracy across three social contexts: face‐to‐face initial interactions, close relationships, and social media. There was clear evidence for cross‐contextual consistency, such that expressive accuracy in face‐to‐face first impressions, based on brief round‐robin interactions, was associated with expressive accuracy with close others (Sample 1; N targets = 514; N dyads = 1656) and based on Facebook profiles (Samples 2 and 3: N targets = 126–132; N dyads = 1170–1476). This was found on average across traits and for high and low observability traits. Further, unique predictors emerged for different types of expressive accuracy, with psychological adjustment and conscientiousness most consistently linked to overall expressive accuracy, extraversion most consistently linked to high observability expressive accuracy, and neuroticism most consistently linked to low observability expressive accuracy. In sum, expressive accuracy appears to emerge robustly and consistently across contexts, although its predictors may differ depending on the type of trait.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".