Should the Default be "Social"? Canada's Pushback against Oversharing by Facebook
Bibliographic record
Abstract
With over 750 million active users worldwide, 1 Facebook has quickly become one of the most highly trafficked websites in the world. 2 Translated into more than seventy languages, 3 and with 70% of user access occurring outside of the United States, 4 the site has truly become an international sensation.As of July 2011, Facebook was worth an estimated $84 billion.5 Along with others like MySpace, LinkedIn, and Twitter, the site has fueled the social networking revolution that is helping to define the new millennium.Facebook's popularity, however, has not come without a price for its users.Although membership is up, privacy control is down.6 As more and more users have joined the site, Facebook has decreased the amount of control users have over their personal data.This is particularly troublesome given the breadth of personal information that the site encourages users to make available (including photos, religious views, hometown, and address) 7 and the growing circle of third-party websites and application developers that can access much of this sensitive user information.8 Threats to user privacy have not gone unnoticed.Outcry over Facebook's privacy policies has echoed worldwide, backed by privacy advocates and a number of lawmakers.9 As Facebook rapidly grows, 10 though, existing privacy and technology laws struggle to keep up with its innovations.11 1 Statistics, FACEBOOK, http://www.facebook.com/press/info.php?statistics (last visited
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.030 | 0.002 |
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".