Are You Paying Your Employees to Cheat? An Experimental Investigation
Bibliographic record
Abstract
Abstract We compare, through a laboratory experiment using salient financial incentives, misrepresentations of performance under target-based compensation with those under both a linear piece-rate and a tournament-based bonus system. An anagram game was employed as the experimental task. Results show that productivity was similar and statistically indistinguishable under the three schemes. In contrast, whether one considers the number of overclaimed words, the number of work/pay periods in which overclaims occur, or the number of participants making an overclaim at least once, target-based compensation produced significantly more cheating than either of the other two systems. While earlier research has compared cheating under target-based compensation with cheating under non-performance-based compensation, which offers no financial incentive to cheat, this is the first study that compares cheating under target-based schemes to cheating under other performance-based schemes. The results suggest that cheating as a response to incentives can be mitigated without giving up performance pay altogether.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".