Just Be Yourself? Effects of an Authenticity Manipulation on Expressive Accuracy in First Impressions
Bibliographic record
Abstract
Does the common advice to “be yourself” lead people to reveal who they truly are? And what broader personal and social implications might this advice bear? In an experimental first-impression study, we examined whether a manipulation instructing some people to be themselves (vs. no explicit instructions) led targets to have their unique personality profiles more accurately perceived, and carried personal and social benefits. Specifically, 204 targets participated in a video interview, with half the targets told to “be yourself” before the interview. Then, 373 observers watched subsets of target video interviews. Overall, the manipulation led targets to be seen with greater distinctive accuracy, especially on their more observable and evaluative self-aspects. However, the manipulation did not significantly influence impression normativity, target likability, nor target post-interview well-being. In sum, being told to be oneself elicits more accurate first-impression perceptions but may not bear immediate personal or social consequences.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".