Bibliographic record
Abstract
Abstract Recognizing the dangers to privacy posed by new technologies, Canada has enacted legislation designed to protect individuals from inappropriate and unwanted uses of their personal information. Public sector privacy laws were enacted in the 1980s, followed by private sector laws in the 1990s. Since 2004, all government and commercial activity in Canada is subject to data protection legislation. Although all the statutes rely to some degree on consent for valid collection, use, and sharing of personal information, the role of consent varies significantly by sector: public sector laws rely on consent as a justification for data collection, We would like to thank Louisa Garib, James Wishart, Janet Lo, Tara Berish, Catherine Thompson, and Martin Saidla for their excellent research support, without which this paper could not have been written. Thanks also to David Matheson, Charles Raab, Marsha Hanen, George Tomlinson, Jena McGill, Jocelyn Cleary, Michael Froomkin, and Teresa Scassa for their helpful comments on a draft version of this paper.
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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 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".