Bibliographic record
Abstract
he defining feature of technological development in the first decade of the twenty first century has been the proliferation of social-networking sites.These sites allow users to connect to their online "friends" and to express themselves through pictures, videos, and verbal notes or comments.Social networking sites have reached unprecedented popularity.At the end of 2009, there were 350 million active users registered on Facebook alone. 1 The widespread popularity of social networking websites has led to a body of law 2 that remains relatively unexamined.What is the admissibility of photographs or comments found on Facebook or other social networking sites?What weight should such "Facebook evidence" 3This paper surveys case law in this area to provide guidance to litigators who wish to utilize social networking technology in their work.It proceeds in three parts.The first part addresses the admissibility of Facebook evidence, discussing the distinction between evidence found on the "public" and "private" elements of the user profile in question, as well as the approach to Facebook postings in the discovery process.The second part surveys Canadian jurisprudence in which Facebook evidence has been prominently featured.It discusses cases in various areas of law, including: torts, family, criminal, and other contexts for trends in courts' approach to Facebook evidence.It also addresses the emerging body of law dealing with service on social networking websites and contains a proposal for the adoption of service on Facebook as a mainstream alternative to personal service.Finally, the third part of the paper contains practical suggestions for counsel dealing with Facebook evidence.be given in family law, cases of personal injury, or criminal proceedings?Can the service of legal documents be effected via Facebook? I. WHAT IS ADMISSIBLE?Numerous cases in torts, family, criminal, and other areas of law have established beyond dispute that Facebook evidence is admissible in Canadian courts.Several outstanding issues remain, however.Disagreement persists among courts with regards to admissibility of postings found in the "private" 4 portion of a user's profile.While some courts have held such evidence admissible, others have refused to order production of such documents.Arguably, the better view is the one which accords with the decision of the Ontario Superior Court of Justice in Schuster v. Royal & Sun Alliance Insurance Co. of Canada, 5 * Ronald Podolny, J. D. (Bronze Medal) (Osgoode Hall,
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.039 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.017 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".