Are You Paying Your Employees to Cheat? An Experimental Investigation
Bibliographic record
Abstract
Canada for generous research support through grants 410-2001-1590 and 410-2007-1380. We are also grateful to J. Atsu Amegashie, Jeremy Clark and Bradley Ruffle for very We compare misrepresentations of performance that occur under a target-based compensation system with those that occur both under a linear piece-rate and a tournament-based bonus setting by means of a controlled laboratory experiment with salient financial incentives. A widely used anagram word-creation game was employed as the experimental task. Results show that whether one considers the number of overclaimed words, the number of work/pay periods in which such over-claims occur, or the number of participants who make an over-claim at least once, target-based compensation produced more cheating than the other two systems. In particular, as argued by Michael Jensen (2003), linear piece-rates produce significantly less cheating than a target-based scheme. Moreover, a tournament scheme based on relative performance also results in significantly less cheating than a target-based one. In addition, as first demonstrated by Schweitzer et al. (2004), cheating is more likely under a target-based scheme the closer a participant is to the target. 1 How people make decisions involving compliance with ethical guidelines and regulations in organizational and social life has been an important focus of research in such diverse fields as philosophy, psychology, accounting, economics, and management. Prior work has identified a number of important factors that affect such compliance decisions by individuals (see Ford & Richardson, 1994; Loe, Farell, & Mansfield, 2000, for comprehensive reviews). These determinants include gender (Ambrose & Schminke,
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".