Appearing Anxious Leads to Negative Judgments by Others
Bibliographic record
Abstract
OBJECTIVES: To investigate people's perceptions of anxiety symptoms and the disclosure of anxiety by others, and how one's own level of social anxiety affects these perceptions. DESIGN: Undergraduate students and community members high (n = 83) and low (n = 80) in social anxiety, as measured by the Social Interaction Anxiety Scale (SIAS; Mattick & Clark, 1998), were randomly assigned to watch 1 of 4 videos in which the target individual either appeared or did not appear anxious, and either disclosed or did not disclose her anxiety. Participants rated the target individual on various characteristics on which socially anxious individuals often fear being judged, and completed measures of their own level of social anxiety, perceived similarity to the target individual, and reasons for their ratings. RESULTS: Participants negatively evaluated others who looked anxious on qualities related to awkwardness, social skills, and weakness, and the disclosure of anxiety was associated with fewer negative judgments. Participants' own levels of social anxiety were not related to their judgments. CONCLUSIONS: This study provided evidence that the concerns held by socially anxious individuals that others will judge them negatively based on their signs of anxiety is accurate to a degree. These findings might inform the process of cognitive restructuring for social anxiety.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".