The Efficacy of Regulation as a Function of Psychological Fit: Reexamining the Hard Law/Soft Law Continuum
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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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".