Le droit de la gouvernance du traitement des données relatives aux personnes physiques : essai de théorisation à partir des cadres juridiques de l’Union européenne et du Canada
Bibliographic record
Abstract
This dissertation advances a conceptualization of the law governing the processing of data relating to natural persons, grounded in a comparative analysis of the legal frameworks of the European Union (EU) and Canada. It argues that the enduring tension between the imperatives of data protection and the free flow of data can be understood through a coherent model of regulation. This model, designated in the thesis as the “law of data processing governance with respect to natural persons,” constitutes an emerging legal construct. It is characterized by its integrative purpose: to ensure, through regulation, the reconciliation of competing interests while upholding fundamental values such as justice, fairness, ethics, and social harmony in the technological processing of data. The methodological approach combines both internal and external legal research, drawing upon the insights of sociology, epistemology, and terminology. It seeks to examine the research object in a transjurisdictional or cross-border manner, through intersecting disciplinary perspectives, with the aim of understanding it, elucidating its underlying logic, and proposing a theoretical framework. The first part of the dissertation provides a critical assessment of the legal definitions of “personal data” in EU law and “personal information” in Canadian law. It highlights epistemological obstacles that justify the need for a reconceptualization based on a more operational analytical framework. It also examines the legal foundations and protections of such data within these legal orders, revealing their internal tensions and conceptual limitations, which call for a more coherent and contextually adapted data governance framework. The second part proposes a model of the law of data processing governance with respect to natural persons, inspired by the EU and Canadian regimes, in order to identify the defining characteristics and structural principles of an operational governance law.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".