PRIVACY WARS IN CYBERSPACE: AN EXAMINATION OF THE LEGAL AND BUSINESS TENSIONS IN INFORMATION PRIVACY
Bibliographic record
Abstract
For all its remarkable attributes, the explosive growth in e-commerce and Internet use has had deleterious consequences for the privacy ofparticipating individuals, who are often unaware of the tremendous amount of information about them that is collected and analyzed These disparate bits of data are amalgamated to yield very identifiable consumer profiles, which are subsequently sold to other organizations, depriving the consumers of their ability to control what they divulge about themselves to others, potentially resulting in a loss of individuality and creativity. Through the use of cookies, which provides numerous benefits to both consumers and retailers, the many advantages of ecommerce applications and business models are realized. However, the reliance on industry selfregulation has led to a plethora ofprivacy infractions in cyberspace, resulting in the enactment of the Canadian Personal Information Protection and Electronic Documents Act (PIPEDA) and the U. S. plan under Bush to introduce privacy legislation after the Federal Trade Commission's recommendation. The task of drafting legislation is wrought with the complexities of balancing the interests of both parties, while attempting to address the tension of employing either overly or under-inclusive language. This difficulty is demonstrated in the analysis of PIPEDA's ambiguities, which is instructive for U S. states seeking to implement similar laws, who should note that privacy legislation ought to mandate full, informed consent through an express and explicit opt-in approach.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.017 | 0.089 |
| Scholarly communication | 0.033 | 0.049 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".