La saisie de données situées dans le nuage en droit criminel canadien
Bibliographic record
Abstract
L’article 8 de la Charte canadienne des droits et libertés prévoit que « chacun a droit à la protection contre les fouilles, les perquisitions ou les saisies abusives ». Cette disposition a fait couler beaucoup d’encre depuis son adoption, mais aussi plus récemment en raison de son application aux nouvelles technologies. En effet, dans les 20 dernières années, la Cour suprême du Canada a adapté les principes généraux découlant des fouilles, saisies et perquisitions aux réalités informatiques nouvelles, notamment l’ordinateur et le cellulaire. Toutefois, l’émergence de nouvelles technologies est un phénomène qui ne cesse jamais. L’essor de l’infonuagique, ce modèle d’utilisation d’Internet qui permet l’accès à des services à distance, incluant notamment la sauvegarde de données sur des serveurs délocalisés, nous force à revoir la protection constitutionnelle qui peut etre accordée aux données personnelles des individus. A travers l’étude des principes généraux applicables aux fouilles, saisies et perquisitions, nous expliquerons pourquoi les données délocalisées sauvegardées grâce à l’infonuagique peuvent être protégées par l’article 8 de la Charte. Nous analyserons également les différentes autorisations judiciaires permettant leur saisie, de même que certains autres principes connexes.\nSection 8 of the Canadian Charter of Rights and Freedoms provides that “everyone has the right to be secure against unreasonable search or seizure”. This provision has been written about extensively since its adoption, but also more recently because of its application to new technologies. In fact, in the last 20 years, the Supreme Court of Canada has adapted the general principles arising from search and seizure law to new technological realities, including computers and cell phones. However, the emergence of new technologies is a phenomenon that never stops. The rise of cloud computing, this model of Internet utilisation that allows access to remote services, including but not limited to data storage on delocalized servers, forces us to review the constitutional protection that can be granted to personal data. Through the study of the general principles applicable to search and seizure, we will explain why delocalized data saved using cloud computing can be protected under section 8 of the Charter. We will also analyze the various judicial authorizations available to obtain this data, as well as certain other related principles.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.021 | 0.014 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".