Ratings of counterproductive performance: the effect of source and rater behavior
Bibliographic record
Abstract
Purpose The purpose of this study was to examine inter‐rater agreement on counterproductive performance between self‐ and peer‐ratings, and the factors that moderate this agreement. The factors investigated included self‐reported levels of counterproductive performance and known antecedents of counterproductive performance: conscientiousness and integrity values. Design/methodology/approach Data were gathered (three to five peer ratings per individual) from 108 undergraduate students. Findings The paper finds that there was a significantly low correlation between self‐ and peer‐ ratings of counterproductive performance. Ratings given by peers were much higher than ratings given by oneself. Individuals and peers who are similar in the extent to which they engage in counterproductive behaviors were in agreement with respect to ratings of counterproductive performance. Practical implications This study provided evidence that rater disagreement is a consistent phenomenon across dimensions of performance. In addition, rater perceptions of counterproductive performance have a significant impact on overall performance ratings; therefore individual differences between the rater and ratee may have a large influence on overall ratings in an organizational setting. There is some evidence in this study that peer ratings of counterproductive behavior vary depending on the rater's own counterproductive behaviors. The fact that rater agreement is influenced by the rater's own behavior implies that individual rater effects are influencing counterproductive performance measurement. Originality/value This study adds value by extending the literature on inter‐rater agreement to counterproductive performance. In addition, this study is unique in that it shows that a rater's own level of counterproductive performance can impact their ratings of others.
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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 | 0.049 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".