Bibliographic record
Abstract
The proliferation of the Internet as a business medium has exacerbated violation of individual privacy. New e-business technologies have increased the ability of online merchants to collect, monitor, target, profile, and even sell personal information about consumers to third parties. Governments, business houses and employers collect data and monitor people, but their practices often threaten an individual's privacy. Because vast amount of data can be collected on the Internet and due to global ramifications, citizens worldwide have expressed concerns over increasing cases of privacy violations. Several privacy groups, all around the world, have joined hands to give a boost to privacy movement. Consumer privacy, therefore, has attracted the widespread attention of regulators across the globe. With the European Directive already in force, “trust seals ” and “government regulations” are the two leading forces pushing for more privacy disclosures. Of course, privacy laws vary throughout the globe but, unfortunately, it has turned out to be the subject of legal contention between the European Union and the United States. The EU has adopted very strict laws to protect its citizens’ privacy, in sharp contrast, to ‘lax-attitude’ and ‘self-regulated’ law of the US. For corporations that collect and use personal information, now ignoring privacy legislative and regulatory warning signs can prove to be a costly mistake. An attempt has been made in this paper to summarize the privacy legislation prevalent in Australia, Canada, the US, the EU, India and Japan. It is expected that a growing number of countries will adopt privacy laws to foster e-commerce.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".