Novelty and the demand for private regulation: Evidence from data privacy governance
Bibliographic record
Abstract
Abstract Private regulations are often presented as low-cost and flexible institutions that can act as policy incubators. In this article, I question under which conditions they go beyond legal compliance and experiment with new rules. Based on a content analysis of 126 data privacy regulations adopted between 1995 and 2016 in the European Union and the United States and thirty-five semistructured interviews, I show that most private regulations include no regulatory novelties. By disaggregating the temporal and spatial distribution of the few novelties, I add nuance to this overall finding and show that private regulations adopted in the United States before 2000 experimented more than others. I argue that this variation reflects the different demands for private regulation in the two jurisdictions and their evolution over time. In the European Union, the early adoption of privacy laws led public regulators and businesses to look for private regulations to reduce transaction costs and thus limited their interest in experimenting with new requirements. In the United States, businesses hoped to gain a first-mover advantage by including new data privacy rules in their private regulations. However, the growing use of private regulations to ease transnational data flows also led to their use as tools to reduce transaction costs.
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.
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 | 0.068 | 0.235 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".