Bringing Security and Intelligence into Focus: How to Clarify the Roles of Newly Created Accountability Bodies
Bibliographic record
Abstract
After unsuccessful attempts to do so in 2005, 2007, 2009, and 2014, the current government is making good on a long-standing Liberal commitment to update and modernize the review, oversight, and accountability for ISAs: it has passed Bill C-22: An Act to Establish the National Security and Intelligence Committee of Parliamentarians (2017), and introduced Bill C-59: An Act Respecting Security Matters (2017). Together these two pieces of legislation establish three new accountability bodies: (1) the National Security and Intelligence Committee of Parliamentarians (NSICOP) will address the lack of parliamentary involvement in intelligence accountability; (2) the proposed National Security and Intelligence Review Agency (NSIRA) would eliminate silos between expert review bodies and increase the number of ISAs subject to independent review; and (3) the proposed Intelligence Commissioner (IC) would approve certain authorizations for CSE and CSIS, as a form of oversight. Together, Bills C-22 and C-59 have the potential to address existing shortcomings in the accountability system and advance innovation across the federal intelligence and security community. However, just how effective C-22 and C-59 will be in remedying these shortcomings depends on how NSICOP and NSIRA will coordinate with each other and other accountability bodies. To that end, this commentary analyses the existing accountability framework, assesses the changes Bill C-22 and C-59 propose, and explains how NSICOP and NSIRA have the potential not only to compensate for shortcomings, but also to enhance and offer innovations to the Canadian national security and intelligence accountability 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 | 0.099 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.070 |
| Scholarly communication | 0.038 | 0.057 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.034 | 0.049 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".