Enforced Standards Versus Evolution by General Acceptance: A Comparative Study of E‐Commerce Privacy Disclosure and Practice in the United States and the United Kingdom
Bibliographic record
Abstract
ABSTRACT We present data on privacy practices in e‐commerce under the European Union's formal regulatory regime prevailing in the United Kingdom and compare it with the data from a previous study of U.S. practices that evolved in the absence of government laws or enforcement. The codification by the E.U. law, and the enforcement by the U.K. government, improves neither the disclosure nor the practice of e‐commerce privacy relative to the United States. Regulation in the United Kingdom also appears to stifle development of a market for Web assurance services. Both U.S. and U.K. consumers continue to be vulnerable to a small number of e‐commerce Web sites that spam their customers, ignoring the latter's expressed or implied preferences. These results raise important questions about finding a balance between enforced standards and conventions in financial reporting. In the second half of the 20th century, financial reporting has been characterized by both a preference for legislated standards and a lack of faith in its evolution as a body of social conventions. Evidence on whether this faith in standards over conventions is justified remains to be marshaled.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".