Bibliographic record
Abstract
A clear, comprehensive, and cutting-edge introduction to the field of information privacy law. Features: A cutting-edge selection of cases and materials that explore issues of emerging technology and information privacy A conceptual framework that brings clarity and accessibility to the wide-ranging field of information privacy lawA useful reference source for any lawyer or professional working in the fieldThorough coverage of information privacy issues, includingmedical and genetic privacy, computer databases, employee monitoring, government data mining, electronic surveillance, anonymity in cyberspace, RFID tags, spam and telemarketing, Internet privacy, spyware, USA-Patriot Act, intelligence gathering and terrorism, consumer and financial privacy, privacy and the media, and moreStimulating pedagogy that raises provocative questions about new technologies and the development of the lawExtensive background informationand authorial guidance that provides clear and concise introductions to various areas of law Clear and engaging discussion of privacy statutes--including summaries oflong and complex privacy statutes, such as the Electronic Communications Privacy Act, Fair Credit Reporting Act, Privacy Act, Freedom of Information Act, Cable Communications Policy Act, HIPAA regulations, and Gramm-Leach-Bliley ActNew to the Fourth Edition: Expanded coverage of new technological developments that have an impact on privacy, including social media, locational information and mobile telephony, and behavioral advertisingGaining access to social media profile pages during discovery Anonymous litigation Expanded coverage of privacy and contract issues Snyder v. Phelps -U.S. Supreme Court case regarding intentional infliction of emotional distress tort and offensive protests at soldier's funerals Updated coverage of the NSA surveillance program cases, including Amnesty International USA v.ClapperNew FTC cases, including Sears, Econometrix,and Google Buzz Safford Unified School District v. Ridding, a U.S. Supreme Court case regarding strip searches at schoolsThe HITECH Act and its impact on health care privacy NASA v. Nelson- U.S. Supreme Court case regarding background questionnaires for employment and the constitutional right to information privacy City of Ontario v. Quon- U.S. Supreme Court case regarding employee reasonable expectations of privacy in electronic communications Coverage of personally identifiable informationLaw enforcement access to GPS cases
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".