Regulating Social Networking: Lessons from Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In recent years, an increasing numbers of internet users have become regular users of on-line social networking services such as Facebook, MySpace, LinkedIn and Second Life. These services provide a social space for users to connect with friends, network with business contacts and create “virtual” alter egos. Regulators have begun to take a particular interest in the privacy practices of social networking services. One of the most significant initiatives in this respect was recently undertaken by the Office of the Privacy Commissioner of Canada, in the form of a 113 page report on its investigation and critique of Facebook’s privacy policies and practices.Notably, the investigation addressed a range of the privacy concerns cited above: (i) collection and use of personal information by third-party application developers; (ii) account deactivation and deletion; (iii) accounts of deceased users; and (iv) the collection of personal information of non-users. This report is worthy of closer examination for a number of reasons. First, it provides useful lessons to both users and providers of social networking services, in identifying and suggesting solutions to certain privacy risk areas. This report also illustrates the willingness of the Office of the Privacy Commissioner of Canada to investigate, and publicly report on, the privacy practices of non-Canadian organizations. Finally, this report provides some significant direction on how overtly the purposes for personal information should be identified to each subject individual.
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.
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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it