Proskauer on Privacy: A Guide to Privacy and Data Security Law in the Information Age
Bibliographic record
Abstract
Todays hodgepodge of privacy and data security standards creates greater compliance burdens for corporations, employers, public agencies, and legal advisers. PLI s Proskauer on Privacy: A Guide to Privacy and Data Security Law in the Information Age reduces those costly burdens. This comprehensive, one-stop reference covers the laws governing every area where data privacy and security is potentially at risk including government records, electronic surveillance, the workplace, medical data, financial information, commercial transactions, and online activity, including communications involving children. Proskauer on Privacy provides essential details on how to develop compliance programs that help your entity satisfy federal and state standards, ensure data privacy and security, prevent cybercrime, and help entities avoid fines, penalties, litigation, damages, and negative publicity. Proskauer on Privacy also examines Europe s rigorous privacy and data security standards, the laws in Canada, Australia, Japan, China, Hong Kong, India, Russia, and Dubai, as well as legal initiatives in California and other states. Updated at least at least once a year, Proskauer on Privacy: A Guide to Privacy and Data Security Law in the Information Age is vital reading for privacy and data security professionals and corporate attorneys, executives, managers, and human resource personnel, as well as for federal and state regulators.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.026 |
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".