Bibliographic record
Abstract
This paper discusses Canada and its ability to wage cyber warfare. Several definitions of cyber warfare are presented and discussed, as well as the motives and potential actors behind a cyber attack. Several definitions of cyberspace are also discussed in order to provide a context for the domain of cyber warfare. A case is then made for why anyone should care about cyber warfare. Cyber attacks are a threat to a nation's security. Cyberspace must be considered a fourth domain of war, with the other three domains being land, air, and sea. There are many dangers within cyberspace that can affect individuals, corporations, and nation-states. Canada's cyber warfare capabilities are then examined. Both offensive and defensive capabilities are considered, with the focus of much of the research being on defensive capabilities. Canada recently released a cyber security strategy which is discussed in detail. Furthermore, capabilities of several government organizations are examined. Finally, a comparative assessment of Canada's capabilities within cyberspace is given. Canada's capabilities are found to be less than adequate to defend against a cyber engagement by an enemy nation-state. However, it is likely that many nation-states would be unable to defend against such an engagement from a knowledgeable and timely attacker.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".