Canada Update May 2010 through July 2010 Highlights of Major Legal News & Significant Court Cases
Bibliographic record
Abstract
FOLLOWING Google's acknowledgement that it has been scanning wireless local area networks ("LANs"), the Privacy Commissioner of Canada began an investigation to determine if this action raised any imposition on Canadians' privacy rights.'In particular, the investigation is to focus on whether Google violated "Canada's private-sector privacy law, the Personal Information Protection and Electronics Documents Act (PIPEDA)."1 2Mining for private consumer information is becoming more commonplace and is one of the fastest-growing internet businesses.3 In this particular situation, Google's vehicles, which it uses to create its ubiquitous StreetView on Google Maps, were scanning wireless networks to gather publicly broadcast SSID [Service Set Identification] information used to identify the WiFi network and the MAC [Medium Access Control] address and correlate a router with a location.4 Google initially reported that it did not collect private, payload-data information sent over the net- *This is Mr. John's first update as Canada Reporter for the Law and Business Review of the Americas.He would like to thank Mr. Andrew Brown, the past reporter, and the graduating staff of the International Law Review Association and wish them well in their endeavors.He also hopes that he can meet the high bar that Mr. Brown has set in his prior updates and the expectations of this year's staff.1.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".