The Impact of User Location on Cookie Notices (Inside and Outside of the\n European Union)
Bibliographic record
Abstract
The web is global, but privacy laws differ by country. Which set of privacy\nrules do websites follow? We empirically study this question by detecting and\nanalyzing cookie notices in an automated way. We crawl 1,500 European,\nAmerican, and Canadian websites from each of 18 countries. We detect cookie\nnotices on 40 percent of websites in our sample. We treat the presence or\nabsence of cookie notices, as well as visual differences, as proxies for\ndifferences in privacy rules. Using a series of regression models, we find that\nthe website's Top Level Domain explains a substantial portion of the variance\nin cookie notice metrics, but the user's vantage point does not. This suggests\nthat websites follow one set of privacy rules for all their users. There is one\nexception to this finding: cookie notices differ when accessing .com domains\nfrom inside versus outside of the EU. We highlight ways in which future\nresearch could build on our preliminary findings.\n
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".