Bibliographic record
Abstract
Canada’s data privacy law, the Personal Information Protection and Electronic Documents Act (PIPEDA), does not require or incentivise de-identification of personal data for purposes of sharing or research. This regulatory lacuna puts Canadian national law at a disadvantage in contrast with the privacy regimes of other countries, such as the United Kingdom, Australia and the United States, all of whom have regulatory language requiring or incentivising de-identification by custodians of personal data. This paper is based on a report commissioned by the Office of the Privacy Commissioner of Canada in service of eventual reform of PIPEDA to include de-identification. The paper addresses terminology, definitions, key debates and policy in other jurisdictions. It recommends legal reform, specific regulatory actions, and investigation of emerging policy strategies and lists remaining open questions for the development of a national Canadian de-identification policy. Chief among these recommendations is a reorientation from a regulatory focus on ‘outputs’ (‘Is the dataset rendered anonymous?’) to a focus on ‘process’ (‘Has the data custodian taken proper steps to reduce identification and privacy risks?’). In part, this is based on a rejection of the possibility of ‘irreversible anonymisation’. Relatedly, the paper argues for requiring a risk management approach to de-identification and for the discouragement of the ‘release-andforget’ model of data disclosure, which relies only on data transformations while ignoring technical, physical, administrative and contractual controls.
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 | 0.038 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.048 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".