The Effect of Information Quantity on Distinctive Accuracy and Normativity of Personality Trait Judgments
Bibliographic record
Abstract
Information quantity is an important moderator of personality judgment accuracy. Some evidence suggests that the amount of available information is positively related to accuracy. The current study utilized the social accuracy model to investigate the effects of differences in thin slices of information quantity on the distinctive accuracy and normativity of personality trait judgments. It was hypothesized that distinctive accuracy and normativity would increase as information quantity increased. Participants were 431 individuals who participated in an online study that varied the length of stimulus target observations (30 seconds, 1 minute, 3 minutes, and 5 minutes), after which judges rated targets using other–report measures of the Big Five personality traits. For all traits combined, significant levels of accuracy were found for all observation lengths, but distinctive accuracy and normativity did not increase as video length increased. Findings varied for individual traits. For distinctive accuracy, there was a linear increase with information quantity for Extraversion and a non–linear relationship for Conscientiousness, while there was a linear decrease for Openness. For normativity, there was a linear increase with information quantity for Agreeableness and a non–linear relationship for Conscientiousness. There are important differences in how observation length affects distinctive accuracy and normativity for different personality traits. © 2019 European Association of Personality Psychology
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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 | 0.004 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".