Bill 207, The Personal Information Protection and Identity Theft Prevention Act
Bibliographic record
Abstract
A. Background InformationIn 1980, the Organization for Economic Co.operation and Development ("OECD") adopted the Guidelines on the Protection of Privacy and Transborder Flows of Personal Data."Canada became a signatory to these guidelines in 1984.5 The guidelines did not necessarily call for legislation, but rather set out principles that should be adhered to either by legislation or voluntary standards.This led to the creation of the Model Code by the Canadian Standards Association in 1996.6 The Model Code set out 10 principles for privacy protection: accountability, identifying purposes, consent, limiting collection, limiting use, disclosure and retention, accuracy, safeguards, openness, individual access, and challenging compliance.7 The Model Code was created after extensive consultation with stakeholders, and it was further envisioned that industries would adapt the code to better fit their circumstances.8 In 1995, the European Union adopted Directive 95/46/EC on the Protection of Individuals with Regard to the Processing of Personal Data and on the Free Movement of Such Data 9 ("the EU Directive").This legislation set out policy that had to be in place for European organizations that collect, use or disclose personal information.This included policy regarding the transferring of this information outside the European Union.Organizations were forbidden from doing so unless the breign country had suitable information protection.10 would even apply to foreign branches of European companies.The EU Directive was to come into effect in 1998, and it was in response to this that the Canadian government enacted PIPEDA in 2000.PIPEDA is essentially a OECD, Guidelines on [he Protection of Privacy and Transborder Flows of Personal Data,
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.025 | 0.010 |
| Insufficient payload (model declined to judge) | 0.037 | 0.036 |
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".