How do people think about the impressions they make on others? The attitudes and substance of metaperceptions.
Bibliographic record
Abstract
= 553), we used the positivity-specificity model to investigate five important aspects of metaperceptions, namely the extent to which (a) metaperceptions reflect metapositivity versus trait-specificity, (b) metapositivity reflects attitudes about the self, (c) the effects of metapositivity and trait-specificity vary across traits and acquaintances, (d) metapositivity helps or hurts meta-accuracy, and (e) metapositivity and trait-specificity are accurate independent of self-perceptions. Overall, participants' ideas about how they were seen included attitudes and substance, but the relative contribution of each depended on the trait being judged and on how well they knew an acquaintance. Participants' ideas about how positively they were seen were related to how positively they saw themselves to varying degrees depending on how much they knew and liked their acquaintances. Participants were also accurate about how positively they were seen and about how they were seen on a given trait, independent of positivity and, with close acquaintances, independent of self-perceptions. The current work demonstrates how the positivity-specificity model can be used to investigate how people think about and have insight into the impressions they make on others. (PsycInfo Database Record (c) 2023 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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| 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 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".