Building New Haystacks: Information Retention and Data Exploitation by the Canadian Security Intelligence Service
Bibliographic record
Abstract
This article examines the technical topic of CSIS’s modern data acquisition, retention and exploitation, a matter not canvassed in the existing legal literature. As part of a special collection on the National Security Act (NSA 2017), it focuses on the policy and legal context driving the NSA 2017 amendments, relying on primary materials to memorialize this background. The paper examines how CSIS has been pulled in divergent directions by its governing law, and sometimes a strained construal of those legal standards, toward controversial information retention practices. It argues that the tempered standards on acquisition, retention and exploitation of non-threat related information created by the NSA 2017 respond to civil liberties objections. The introduction of the “dataset” regime in the NSA 2017 may finally establish an equilibrium between too aggressive an information destruction standard that imperils due process and too constraining an information retention system that undermines CSIS’s legitimate intelligence functions. The article flags, however, areas of doubt, the resolution of which will have important implications for the constitutionality and legitimacy of the new system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.024 | 0.041 |
| Scholarly communication | 0.025 | 0.015 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".