Do You Know Where Your Data Is? A Study of the Effect of Enforcement Strategies on Privacy Policies
Bibliographic record
Abstract
Numerous countries around the world have enacted privacy-protection legislation, in an effort to protect their citizens and instill confidence in the valuable business-to-consumer E-commerce industry. These laws will be most effective if and when they establish a standard of practice that consumers can use as a guideline for the future behavior of e-commerce vendors. However, while privacy-protection laws share many similarities, the enforcement mechanisms supporting them vary hugely. Furthermore, it is unclear which (if any) of these mechanisms are effective in promoting a standard of practice that fits with the social norms of those countries. We present a large-scale empirical study of the role of legal enforcement in standardizing privacy protection on the Internet. Our study is based on an automated analysis of documents posted on the 100,000 most popular websites (as ranked by Alexa.com). We find that legal frameworks have had little success in creating standard practices for privacy-sensitive actions.
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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 | 0.031 | 0.270 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".