Gerrymandering the National Security Narrative: A Case Study of the Canadian Security Intelligence Service’s Handling of its Bulk Metadata Exploitation Program
Bibliographic record
Abstract
In November of 2016, the Federal Court of Canada published a scathing ruling pertaining to some of the Canadian Security Intelligence Service’s (CSIS) big data surveillance activities. Among other charges of wrongdoing, the ruling accused the agency of not having been forthcoming with the Court about the existence of its “Operational Data Analysis Center” (ODAC), an advanced analytics bulk metadata exploitation program that had been operational since 2006. The ruling also revealed that a significant portion of the metadata collected by CSIS should not have been retained in ODAC, a practice that the ruling declared illegal. Drawing from the ruling, a series of classified CSIS documents obtained via requests made under the Access to Information Act, various public reports from both CSIS and the Security Intelligence Review Committee, as well as a Senate Committee hearing transcript, this article examines CSIS’s conduct, justifications, and statements relating to its bulk metadata retention activities spanning from the year of ODAC’s inception in 2006 to the publication of the ODAC ruling in 2016. The paper demonstrates how CSIS engaged in various forms of secrecy and how it successfully constructed and disseminated its own big data related language to effectively “gerrymander” the national security narrative, thereby ultimately ensuring its tight control over the knowledge that the Court would have of big data surveillance and CSIS’s engagement with it. This would enable the Service to keep its metadata exploitation program out of sight and operational for ten years until the Court ended up declaring a significant portion of ODAC illegal in 2016.
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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.100 | 0.031 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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".