Bibliographic record
Abstract
National security has always been a fundamental responsibility of the modern state.However, in Canada as in many other countries, the national security and intelligence community has grown in power, resources, and reach since 2001.New organizations have been created, others have been merged and, as recently as 2019, the mandates of some of its core agencies have been expanded.The kind of technological innovation that has led to the emergence of new threats in recent years is also providing security and intelligence professionals with new and potentially more intrusive tools to practise their craft.Essential to our collective safety, security and intelligence agencies also operate with considerable secrecy as they wield powers and tools that, if used inappropriately, can be injurious to our fundamental rights.For these reasons, ensuring their democratic accountability and the legality of their actions is as vital as it is challenging.In sum, the agencies entrusted with security and intelligence functions have been fast evolving, their effectiveness remains vital to the interests of Canadians, and their operation raises significant challenges of democratic governance.Given this importance, it is surprising that the Canadian national security and intelligence community is not better known.Even taking into account the difficulties created by its secrecy, the academic literature dedicated to understanding its practices, challenges, and impact remains remarkably limited.It is partly for this reason that Top Secret Canada is such a welcome addition to the IPAC Series in Public Management and Governance.By providing a comprehensive overview of the state of the national security and intelligence community as it stands in 2020, this volume will not only be of interest to specialists seeking an up-to-date scan of the latest developments, key trends, and challenges faced by the field's professionals, but it will also provide an invaluable one-stop overview of this sector to a broader readership of public
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.605 | 0.532 |
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".