Accounting for the Future or the Past?: Developing Accountability and Oversight Systems to Meet Future Intelligence Needs
Bibliographic record
Abstract
Abstract This article discusses the development of accountability and intelligence culture. It begins with the contentious issues that have prevailed in the field of intelligence. It defines the use of certain terms such as accountability and responsibility within the context of intelligence. The article also looks at how systems of oversight and accountability have developed in Canada's longest and most enduring intelligence partners. The focus here is on the causes, legislative practices, and shortcomings. Following the discussion on the systems of oversight and accountability in Canadian intelligence, the article proceeds with a discussion on how Canada has developed its own systems. The emphasis here is on the external procedures and independent institutions. The purpose in this section is twofold: first, is to illustrate that even close allies have followed different paths and, second, is to show that Canada, while initially getting off to a sound start, has failed to keep pace not only with its key intelligence allies but also with the changing threat environment. Finally, the article suggests what a system of oversight and accountability that will meet Canada's future needs might look like and what it would do.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".