Are Shy Adults Really Bolder Online? It Depends on the Context
Bibliographic record
Abstract
We examined whether individual differences in shyness and context influenced the amount of computer-mediated self-disclosure and use of affective language during an unfamiliar dyadic social interaction. Unfamiliar young adults were selected for high and low self-reported shyness and paired in mixed dyads (one shy and one nonshy). Each dyad was randomly assigned to either a live webcam or no webcam condition. Participants then engaged in a 20-minute online free chat over the Internet in the laboratory. Free chat conversations were archived, and the transcripts were objectively coded for traditional communication variables, conversational style, and the use of affective language. As predicted, shy adults engaged in significantly fewer spontaneous self-disclosures than did their nonshy counterparts only in the webcam condition. Shy versus nonshy adults did not differ on spontaneous self-disclosures in the no webcam condition. However, context did not influence the use of computer-mediated affective language. Although shy adults used significantly less active and pleasant words than their nonshy counterparts, these differences were not related to webcam condition. The present findings replicate and extend earlier work on shyness, context, and computer-mediated communication to a selected sample of shy adults. Findings suggest that context may influence some, but not all, aspects of social communication in shy adults.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".