Do you make a better or worse impression than you think?
Bibliographic record
Abstract
= 1,336), we indexed metabias (i.e., the mean-level difference between metaperceptions and impressions) on a broad range of attributes to test: (a) how biased people are on average, (b) whether bias is pervasive or limited to particular contexts (level of acquaintanceship) or attributes (e.g., liking judgments or traits), (c) whether bias is consistent across attributes, and (d) what explains bias. On average, participants demonstrated a negative metabias on most attributes for both new and well-known acquaintances, suggesting that people generally fail to appreciate how positively they are seen by others. However, there was variability around this average such that, whereas most participants were negatively biased (48%), many were accurate (34%), and some were positively biased (18%). Bias was also consistent across traits, suggesting that knowing people's metabias for one attribute offers some insight into their relative bias for other attributes. What explained metabias? Generally, people relied too much on their self-perceptions, which were more negative than the impressions they made, although bias for new acquaintances involved additional factors. That said, people understood that others saw them more positively than how they saw themselves, but they did not understand the extent of this positivity. These results offer a general framework for understanding metabias and add to the growing literature, demonstrating that people are not positively biased. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".