License to Cheat: Voluntary Regulation and Ethical Behavior
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
Although monitoring and regulation can be used to combat socially costly unethical conduct, their intended targets can often avoid regulation or hide their behavior. This surrenders at least part of the effectiveness of regulatory policies to firms' and individuals' decisions to voluntarily submit to regulation. We study individuals' decisions to avoid monitoring or regulation and thus enhance their ability to engage in unethical conduct. We conduct a laboratory experiment in which participants engage in a competitive task and can decide between having the opportunity to misreport their performance or having their performance verified by an external monitor. To study the effect of social factors on the willingness to be subject to monitoring, we vary whether participants make this decision simultaneously with others or sequentially, as well as whether the decision is private or public. Our results show that the opportunity to avoid being submitted to regulation produces more unethical conduct than situations in which regulation is either exogenously imposed or entirely absent. This paper was accepted by Uri Gneezy, behavioral economics.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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