When is personal data “about” or “relating to” an individual? A comparison of Australian, Canadian, and EU data protection and privacy laws
Bibliographic record
Abstract
The definition of “personal information” or “personal data” is foundational to the application of data protection laws. One aspect of these definitions is that the information must be linked to an identifiable individual, which is incorporated in the requirement that the information must be “about” or “relating to” an individual. This article examines this requirement in light of recent judicial and legislative developments in Australia, Canada and the European Union. In particular, it contrasts the decisions rendered by the Federal Court of Australia in Privacy Commissioner v Telstra Corporation Ltd and by the European Court of Justice decisions in Scarlet Extended and Patrick Breyer v Bundesrepublik Deutschland as well as the new General Data Protection Regulation with Canadian law. This article also compares how the three jurisdictions deal with the vexed issue of IP addresses as personal information where the connection between the IP address and a particular individual often raises particular problems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".