"O, Privacy" Canada's Importance in the Development of the International Data Privacy Regime
Bibliographic record
Abstract
As e-commerce and international outsourcing rapidly expands, the governments of the leading Western industrial nations are making every effort to balance protection of their citizens’ personal information with governmental interests in national security and promotion of commercial competitiveness. A continuously developing body of international law—to which we refer as the International Data Privacy (IDP) regime—attempts to reconcile the rights and conduct of the three major actors in this regime: governments, businesses, and individuals whose information is gathered, stored or traded. Famously, Europe and the United States have drastically different notions of what constitutes the proper balance of rights and responsibilities of the three actors, and approaches to achieving that balance. On the one hand, European governments are focused on protecting their citizens’ privacy and ensuring that those individuals have enough faith in the safety of the international system to freely engage in commerce, especially e-commerce. On the other hand, national security concerns in the United States, particularly after September 11, 2001, push the limits of civil rights such as privacy in order to satisfy the State’s duty to protect its citizens. Canada has traditionally taken a middle-of-the-road approach to this cross-Atlantic divide. However, since the enactment of the EU Data Privacy Directive of 1995 (“EU Directive”), Canada has been moving closer toward the European model of the IDP regime, and will
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.025 | 0.023 |
| Scholarly communication | 0.023 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".