Bibliographic record
Abstract
Undoubtedly, the government, business houses and employers have a legitimate need to collect data and to monitor people, but their practices often threaten an individual's privacy. Since a vast amount of data can be collected on the Internet, and due to its global ramifications, the FTC had identified ‘core’ principles of privacy which are widely accepted by leading countries. With the European Directive in force from 1998, ‘trust seals’ and ‘government regulations’ are the two leading forces pushing for more privacy disclosures. The need for companies to develop and put into place good privacy policies and/or statements has become more crucial than ever. Privacy legislation prevalent in the US, the EU, Canada, Japan and Australia is summarized in this article. Privacy laws vary throughout the globe but, unfortunately, the topic has turned out to be the subject of legal contention between the EU and the US. Among the companies given high marks by privacy advocates for making data protection a priority are Dell, IBM, Intel, Microsoft, Procter & Gamble, Time Warmer and Verizon. Currently, the only way consumers can stop the collection of their personal data is to ‘opt-out’ or configure the browser to reject ‘cookies’. We have briefly examined various methods (like Carnivore program, W3C Platform for Privacy Preferences (P3P), Encryption, etc.) used by the corporate world. Today, more advanced technological safeguards are needed. For corporations that collect and use personal information, ignoring privacy legislative and regulatory warning signs can prove to be a costly mistake.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".