Privacy in the digital age : comparative analysis of data protection policy dynamics in Belgium, Canada and Switzerland
Bibliographic record
Abstract
Over the last three decades, with the development of the Internet and information and communication technologies (ICT), the number of ways of collecting and processing personal data has exploded. In the digital age, privacy issues have become more internationalised, more technically complex and therefore less transparent to non-experts. As a result, the traditional privacy regulation of the liberal nation state became progressively obsolete. By studying the broad evolution of state protection of this fundamental right since the emergence of the World Wide Web in 1989, and particularly after a mass surveillance focusing event with worldwide impact, this research aims to understand why and how does the privacy issue reach the agenda in the digital age. Theoretically, this research proposes an original analytical model focused especially on the political and societal attention given to the privacy issue. Methodologically, this work makes use of a comparative multi-method design, combining process tracing and qualitative comparisons, based on a triangulation of sources (official documents, press, and interviews with experts). Empirically, this work will trace the general evolution of the data protection regulation in Belgium, Canada and Switzerland – three countries differentiated on the European dimension – in the digital age and try to explain their different policy responses to the Snowden revelations that started in 2013.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".