Le couplage de données et la protection de la vie privée informationnelle sous l'article 8 de la Charte canadienne /
Bibliographic record
Abstract
Data matching is the automated process permitting the comparison of significant amounts of personal data from two or more different databanks in order to produce new information. Its use by governments implicates many rights and freedoms, including the protection against unreasonable search and seizure under section 8 of the Canadian Charter. In the author's opinion, a governmental data matching program will probably constitute a search or seizure under section 8 when a positive answer is given to two questions. First, is there a use or transfer of information which implicates constitutionally protected information? Generally, section 8 will only protect biographical personal information, as described in the Plant case. Second, one must determine if a reasonable expectation of privacy exists as to the purpose for which the information will be used. In other words, one must determine if the two governmental databanks are separate on the constitutional level. However, a positive answer to both of theses questions does not mean that the matching program necessarily infringes section 8. It will not be considered unreasonable if it is authorised by law, if the law itself is reasonable, and if the execution of the program is reasonable. Presuming that the program is authorised by law, it is probable that a matching program aimed to detect individuals collecting illegally social benefits will not be considered unreasonable.
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 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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads agree on what is shown here.
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".