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.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.026 | 0.016 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".