The Efficacy of Regulation as a Function of Psychological Fit: Reexamining the Hard Law/Soft Law Continuum
Why this work is in the frame
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Bibliographic record
Abstract
Much of the legal literature discusses regulation and regulatory forms with a seemingly implicit assumption that "those to be influenced" are inherently self-interested and thus motivated to comply with legal structures only when there are sufficient external incentives to do so. This view of the person is inconsistent with recent perspectives in the field of psychology. A law and morality perspective, coupled with insights from the field of psychology, asserts that influence, compliance, and motivation are far more complex than this legal literature would suggest. In this Article, we map the varying influence structures, motives, psychological needs, emotional mechanisms, and levels of moral reasoning that various forms of regulation, from hard law to soft law, might appeal to. We provide examples from global banking and one soft law initiative, the Equator Principles, to illustrate reasons psychology would suggest why soft law may be more effective in some circumstances in influencing behavior within the firm than hard law, while recognizing important limits to such influence.
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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 | 0.002 | 0.001 |
| 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 | 0.070 |
| 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