Through the looking glass clearly: Accuracy and assumed similarity in well-adjusted individuals' first impressions.
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
Do well-adjusted individuals have particularly accurate insight into what others are like or are they biased, primarily seeing their own characteristics in others? In the current studies, the authors examined how psychologically adjusted individuals tend to see new acquaintances, directly comparing their levels of distinctive accuracy (accurately perceiving others' unique characteristics), normative accuracy (perceiving others as similar to the average person), and assumed similarity (perceiving others as similar to the self). Across two interactive, round-robin studies, well-adjusted individuals, compared with less adjusted individuals, did not perceive new acquaintances' unique characteristics more accurately but did perceive new acquaintances, on average, as similar to the average person, reflecting an accurate understanding of what people generally tend to be like. Furthermore, well-adjusted individuals had a biased tendency to perceive their own unique characteristics in others. Of note, both pre-existing perceiver adjustment and target-specific liking independently predicted greater accuracy and assumed similarity in first impressions. In sum, well-adjusted individuals see through the looking glass clearly: although they erroneously see others as possessing their own unique characteristics, they accurately understand what others generally tend to be like.
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it