Bibliographic record
Abstract
This Article proposes an economic framework with which to analyze the U.S.'s electronic privacy laws in the context of international privacy standards.A key assumption is that electronic privacy generally exists in tension with the speed and convenience of e-commerce: if privacy protections are too strong, e-commerce will suffer.At the same time, however, this Article shows that consumers expect a certain basic level of privacy when they conduct electronic transactions.A government that fails to provide this certain level of privacy effectively weakens the e-commerce industry.This Article concludes the United States has failed to guarantee sufficient privacy protections and that, by learning from the E.U. and Canada, the U.S. can increase both personal privacy and the effectiveness of e-commerce by enacting comprehensive electronic privacy laws.8 "Who will watch the watchmen?"9 There are many excellent sources that summarize the E.U., U.S., and Canadian privacy law in much greater detail than is afforded here.E.g., Avner Levin & Mary Jo Nicholson, Privacy Law in the United States, the E.U. and Canada: The Allure of the Middle Ground, 2 OTTAWA L
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".