Your Best Self Helps Reveal Your True Self
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
How does trying to make a positive impression on others impact the accuracy of impressions? In an experimental study, the impact of positive self-presentation on the accuracy of impressions was examined by randomly assigning targets to either “put their best face forward” or to a control condition with low self-presentation demands. First, self-presenters successfully elicited more positive impressions from others, being viewed as more normative and better liked than those less motivated to self-present. Importantly, self-presenters were also viewed with greater accuracy than control targets, being perceived more in line with their self-reported distinctive personality traits and their IQ test scores. Mediational analyses were consistent with the hypothesis that self-presenters were more engaging than controls, which in turn led these individuals to be viewed with greater distinctive self–other agreement. In sum, positive self-presentation facilitates more accurate impressions, indicating that putting one’s best self forward helps reveal one’s true self.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it