The Case for a U.S. Privacy Commissioner: A Canadian Commissioner’s Perspective, 19 J. Marshall J. Computer & Info. L. 1 (2000)
Bibliographic record
Abstract
The demands of social democratic government, the growth of electronic commerce, and the advance of technology have fueled the debate over internet privacy. Technology offers unprecedented opportunities but can also become tools of abuse. Debate in the United States centers around the conflicting interests of industry self-control versus government regulation. Technological and market-based solutions are ineffective because they lead to inadequate and inconsistent protection. Many user-driven privacy choices can impede the growth of consumer trust. Voluntarily adopted privacy policies are either extremely limited or easily circumvented with tracking technology that allows no consumer control over the collection of their personal data. Incompatible national standards can disrupt data flow. The United States could address these concerns by shifting away from its industry and state-based regulatory model to one based on fair information practices, with oversight assigned to a single agency controlled by a U.S. Privacy Commissioner who could work with international officials to resolve complex privacy issues.
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 | 0.025 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.051 | 0.043 |
| Scholarly communication | 0.037 | 0.018 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.046 | 0.025 |
| Insufficient payload (model declined to judge) | 0.010 | 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".