Revisiting the governance of privacy: Contemporary policy instruments in global perspective
Bibliographic record
Abstract
Abstract The repertoire of policy instruments within a particular policy sector varies by jurisdiction; some “tools of government” are associated with particular administrative and regulatory traditions and political cultures. It is less clear how the instruments associated with a particular policy sector may change over time, as economic, social, and technological conditions evolve. In the early 2000s, we surveyed and analyzed the global repertoire of policy instruments deployed to protect personal data. In this article, we explore how those instruments have changed as a result of 15 years of social, economic and technological transformations, during which the issue has assumed a far higher global profile, as one of the central policy questions associated with modern networked communications. We review the contemporary range of transnational, regulatory, self‐regulatory, and technical instruments according to the same framework, and conclude that the types of policy instrument have remained relatively stable, even though they are now deployed on a global scale. While the labels remain the same, however, the conceptual foundations for their legitimation and justification are shifting as greater emphases on accountability, risk, ethics, and the social/political value of privacy have gained purchase. Our analysis demonstrates both continuity and change within the governance of privacy, and displays how we would have tackled the same research project today. As a broader case study of regulation, it highlights the importance of going beyond technical and instrumental labels. Change or stability of policy instruments does not take place in isolation from the wider conceptualizations that shape their meaning, purpose, and effect.
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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 | 0.023 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.056 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".