The Effects of Dynamic Behavioral Trends and Personality Traits on Performance Appraisals
Bibliographic record
Abstract
Two field studies examined the effects of dynamic performance trends, effort trends, and personality on evaluations of overall performance, effort, and ratee attributes. These effects were examined with a sample of 199 university football players (Study 1) and 217 undergraduate and MBA students (Study 2). Study 1 found an interaction between performance trends and personality: rising performance trends resulted in higher overall appraisals. However, performance trends had a stronger effect for players who exhibited socially undesirable traits, while socially desirable players received good overall evaluations regardless of their performance trends. Study 2 found that trends in effort affected overall evaluations of contextual performance, but did not affect evaluations of task performance. Contrary to prior lab research on performance trends, the results suggest that, in the field, raters are surprisingly good at integrating appropriate trend information. Because raters interact with their ratee over time, they do not need to make inferences about the ratee’s personality based on the ratee’s performance trend–they have that information at hand. Implications for cognitive theories of performance appraisal and future research are discussed.
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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 | 0.003 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".