The Compliance Consequences of Fault Assignment and Sanction Strength in Sanctions
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 Regulators rely heavily on “no-fault” settlements in their enforcement, where targets avoid costly litigation by accepting sanctions without admitting or denying fault. Considerable public debate surrounds the issue, but prior research has typically focused on financial dimensions of sanctions such as the magnitude of fines. I conduct an economic experiment where individuals face a costly compliance choice in the presence of sanctions that may either be greater than or less than the benefits of violating and may also require admission of fault. I observe that compliance quality is greater when sanctions assign fault. I also observe that, relative to strong sanctions, the frequency of compliance decreases under weak no-fault sanctions but does not decrease under weak fault sanctions. Lastly, I observe that non-decision-making participants struggle with the task of anticipating compliance, believing that compliance quality will increase in sanction strength but not fault although the opposite is true. Data Availability: Data are available on request from the author.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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