Knowing how you see me: Exploring meta‐accuracy of personality, emotions and values and their links with relationship well‐being among young adults
Bibliographic record
Abstract
INTRODUCTION: Do people (i.e., metaperceivers) know their romantic partners' (i.e., perceivers') impressions, displaying meta-accuracy? Is it related to relationship well-being? We explored two components of meta-accuracy: (1) positive meta-accuracy (i.e., knowing the perceiver's positive impressions of the metaperceiver), and (2) distinctive meta-accuracy (i.e., knowing the perceiver's unique impressions of the metaperceiver). First, we compared baseline levels of each component across three domains (personality, emotions, values), and, second, examined and compared their links with relationship well-being. METHOD: A sample of 205 romantic couples were recruited. The Social Accuracy Model was adapted for analyses. RESULTS: Metaperceivers displayed both positive and distinctive meta-accuracy across all domains, and displayed greater positive emotion meta-accuracy and distinctive personality meta-accuracy compared to the other domains. Positive meta-accuracy, in general, was related to metaperceivers' relationship well-being and distinctive meta-accuracy, in general, was related to relationship well-being for metaperceivers and perceivers. Further, positive personality meta-accuracy was associated with relationship well-being for metaperceivers, and positive emotion meta-accuracy was associated with relationship well-being for metaperceivers and perceivers. CONCLUSION: Overall, the present research broadens the meta-accuracy literature by expanding it to a novel domain (values) and highlighting the relative contributions of domains that has been previously explored in isolation (personality and emotions).
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".