Giving reasons as a means to enhance compliance with legal norms
Bibliographic record
Abstract
The threat of sanctions is often insufficient to ensure compliance with legal norms. Recently, much attention has been given to nudges – choice-preserving measures that take advantage of people’s automatic System 1 thinking – as a means of influencing behaviour without sanctions, but nudges are often ineffective and controversial. This article explores the provision of information about the reasons underlying legal norms, as a means to enhance compliance, primarily through deliberative System 2 thinking. While the idea that legal norms should be accompanied by explanatory preambles – to complement the law’s threat of sanctions with persuasion – goes back to Plato, this technique is not commonly used nowadays, and scholars have failed to systematically consider this possibility. The article argues that reason giving can enhance compliance and reduce the need for costly enforcement mechanisms. The theoretical part of the article comprises three parts. It first describes the mechanisms through which reasons may influence people’s behaviour. It then distinguishes between reason giving as a means to enhance compliance and as a means to attain other goals and between reason giving and related means to enhance compliance. Finally, it discusses various policy and pragmatic considerations that bear on the use of reason giving. Following the theoretical discussion, the empirical part of the article uses vignette studies to demonstrate the feasibility and efficacy of the reason-giving technique. The results of these new studies show that providing good reasons for legal norms enhances people’s inclination to comply with them, in comparison to not providing the reasons underlying the norms. However, whereas persuasive reasons may promote compliance, questionable reasons might reduce it. We call on scholars and policy makers to pay more attention to this readily available measure of enhancing compliance with norms.
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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 | 0.022 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".