Are You Paying Your Employees to Cheat? An Experimental Investigation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it