Accounting for the Future or the Past?: Developing Accountability and Oversight Systems to Meet Future Intelligence Needs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it