Bibliographic record
Abstract
in this issue of Global Privacy Law Review (GPLR).The Articles section contains two interesting and relevant pieces.They address some of the fundamental concepts of data privacy laws in Canada and the EU, respectively.Kicking things off, Xavier Dionne of the University of Montreal analyses and aims to define the concept of 'collection of personal information' in Canada. 1 He considers recent amendments to privacy laws, case law, and investigations by privacy commissioners.Canada's privacy laws are comprised of a complex set of federal and provincial laws.Some are of general application, while others are sector-specific, such as health privacy, anti-spam, and consumer protection laws.Accordingly, the definitions differ across sectors and territories.The concept of 'collection' of personal information has no consistent definition in Canada.The federal Personal Information Protection and Electronic Documents Act (PIPEDA) defines personal information as 'information about an identifiable individual (renseignement personnel)'. 2It includes any factual or subjective information, recorded or not, about an identifiable individual. 3However, the 'collection' of personal information is not defined under the PIPEDA.Conversely, under Alberta's Health Information Act 'collect' means to 'gather, acquire, receive or obtain health information'. 41
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".