Blending in at the Cost of Losing Oneself: Dishonest Self-Disclosure Erodes Self-Concept Clarity in Social Anxiety
Bibliographic record
Abstract
Self-concept clarity helps to promote self-esteem and guide adaptive social behavior. Recent studies have found that people with higher levels of trait social anxiety exhibit significantly diminished levels of self-concept clarity, but the mechanisms that might link higher social anxiety with lower self-concept clarity are untested and unknown. We propose that the relation between social anxiety and self-concept clarity is mediated by dishonest self-disclosure – a self-protective strategy in which one asserts an inauthentic or dishonest opinion to others based on what one believes others wish to hear rather than one's own genuine viewpoint. To test this prediction, we manipulated the honesty of participants' self-disclosures during a social task in the laboratory and measured subsequent changes in self-concept clarity. As hypothesized, dishonest relative to honest self-disclosure led to significantly reduced levels of self-concept clarity, but only amongst participants with higher levels of trait social anxiety. These findings help to elucidate the processes underlying the link between social anxiety and self-concept clarity and provide insight into the costs of adopting an inauthentic façade during interpersonal encounters when social conformity motives become salient.
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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 | 0.005 |
| 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 | 0.000 | 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".