BALANCING NATIONAL SECURITY INTERESTS WITH PRIVACY, THE CHARTER AND RULE OF LAW: BUILDING CHECKLISTS INTO OPERATION PLANNING
Bibliographic record
Abstract
On November 17, 2023, Justin Dubois, Executive Director and General Counsel of the Office of the Intelligence Commissioner, presented Balancing National Security Interests with Privacy, the Charter and Rule of Law: Building Checklists into Operation Planning for this year’s West Coast Security Conference. The presentation was followed by a question-and-answer period with questions from the audience and CASIS Vancouver executives. The key points discussed were the Intelligence Commissioner Act, the role of the Intelligence Commissioner in providing quasi-judicial review of certain activities of intelligence agencies, and the Intelligence Commissioner’s use of the principles of necessity, proportionality, and reasonableness to guide its decision-making process.
 
 Received: 01-04-2024
 Revised: 01-29-2024
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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 | 0.120 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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".