Bibliographic record
Abstract
Contents: Introduction. Part I Identity, Security and Privacy in Context: Designing for trust, L. Jean Camp The digital persona and its application to data surveillance, Roger Clarke Privacy, visibility, transparency, and exposure, Julie E. Cohen Exploring identity and identification in cyberspace, Oscar H. Gandy, Jr A contextual approach to privacy online, Helen Nissenbaum. Part II Surveillance, Security and Anonymity: Hacking the Panopticon: distributed online surveillance and resistance, Benoit Dupont The surveillant assemblage, Kevin D. Haggerty and Richard V. Ericson 'Sousveillance': inverse surveillance in multimedia imaging, Steve Mann Surveillance as cultural practice, Torin Monahan Surveillance and security: a dodgy relationship, Walter Peissl Counter-surveillance as political intervention?, Torin Monahan Resistance against cyber-surveillance within social movements and how surveillance adapts, Oliver Leistert Privacy, surveillance, and law, Richard A. Posner Squaring the circle of smart surveillance and privacy, Joseph A. Cannataci. Part III Privacy, Data Protection and Security: Data protection pursuant to the right to privacy in human rights treaties, Lee A. Bygrave Consumer culture and the commodification of policing and security, Ian Loader Information technology and dataveillance, Roger A. Clarke Public assessment of new surveillance-oriented security technologies: beyond the trade-off between privacy and security, Vincenzo Pavone and Sara Degli Esposti European protectionism in cloud computing: addressing concerns over the PATRIOT Act, John T. Billings Internet intermediaries, cloud computing and geospatial data: how competition and privacy converge in the mobile environment, Lisa Madelon Campbell Stolen identities, Jennifer Whitson and Kevin D. Haggerty. Part IV Smart Technologies, Social Control and Human Rights: The body as data? Biobank regulation via the 'back door' of data protection law, Lee A. Bygrave CCTV policy in the UK: reconsidering the evidence base, William Webster Camera surveillance within the UK: enhancing public safety or a social threat?, Barrie Sheldon Privacy and data protection at the time of facial recognition: towards a new right to digital identity?, Shara Monteleone The deciders: the future of privacy and free speech in the age of Facebook and Google, Jeffrey Rosen Affordance, conventions, and design, Donald A. Norman eHealth: roadmap to finding a successful cure for privacy issues, Deborah C. Peel There's no app for that: protecting users from mobile service providers and developers of location-based applications, Daniel L. Pieringer The Privacy Bill of Rights: what are the expectations for 2013?, Christopher Wolf. Name index.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".