Hater_etal_The Social Meta-Accuracy Model_JPSP_preprint
Bibliographic record
Abstract
To what extent do individuals differ in understanding how others see them and who is particularly good at it? Answering these questions about the “good meta-perceiver” is relevant given the beneficial outcomes of meta-accuracy. However, there likely is more than one type of the good meta-perceiver: one who knows the specific impressions they make more than others do (dyadic meta-accuracy) and one who knows their reputation more than others do (generalized meta-accuracy). To identify and understand these good meta-perceivers, we introduce the Social Meta-Accuracy Model (SMAM) as a statistical and conceptual framework and apply the SMAM to four samples of first impression interactions. As part of our demonstration, we also investigated the routes to and the correlates of both types of good meta-perceivers. Results from SMAM show that, overall, people were able to detect the unique and general first impressions they made, but there was little evidence for individual differences in dyadic meta-accuracy in a first impression. In contrast, there were substantial individual differences in generalized meta-accuracy, and this ability was largely explained by being transparent (i.e., good meta-perceivers were seen as they saw themselves). We also observed some evidence that good generalized meta-perceivers in a first impression tend to be extraverted and popular. This work demonstrated that the SMAM is a useful tool for identifying and understanding both types of good meta-perceivers and paves the way for future work on individual differences in meta-accuracy in other contexts.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.006 |
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".