Responding to Deficiencies in the Architecture of Privacy: Co-Regulation as the Path Forward for Data Protection on Social Networking Sites
Bibliographic record
Abstract
Social Networking Sites like Facebook, Twitter and the like are a ubiquitous part of contemporary culture. Yet, as exemplified on numerous occasions, most recently in the Cambridge Analytica scandal that shook Facebook in 2018, these sites pose major concerns for personal data protection. Whereas self-regulation has characterized the general regulatory mindset since the early days of the Internet, it is no longer viable given the threat social media poses to user privacy. This article notes the deficiencies of self-regulatory models of privacy and contends jurisdictions like Canada should ensure they have strong data protection regulations to adequately protect the public. However, while underscoring the economic value of Big Data technologies, it posits regulation does not necessarily need to come at the cost of economic prosperity. By adopting a co-regulatory model based on regulatory negotiation, various stakeholders can come together and draft robust and flexible data protection regulations, including both tailored rules and oversight mechanisms. Beginning with a survey of the challenges and opportunities of Big Data and social networking sites (I), this article then canvasses the data protection framework of three jurisdictions, namely the United States, Canada, and the European Union (II). Finally, it shows the clear advantages of co-regulation as a regulatory paradigm and offers an outline for the regulation of social networking sites using regulatory negotiation (III).
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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 | 0.096 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.088 |
| Scholarly communication | 0.036 | 0.045 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.021 | 0.028 |
| 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; 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".