Cyber Capacity Building in the Canadian Arctic and the North
Bibliographic record
Abstract
The Canadian Arctic cyber domain is set to rapidly expand in the next decade with emerging security vulnerabilities that would benefit from a multi-stakeholder Arctic Cyber Security Ecosystem. Great power competition will affect the Arctic as the United States, Russia, and now China seek to influence the resource rich region. Cyber is not only a matter of defence, but it is interconnected with education and economic development. The threat of disinformation is an example of how new ways of warfare can impact Canada through the Arctic. Cyber capacity building (CCB) could include domestic cyber education, skills training, and investment in scientific and technical (S&T) and information technology (IT) infrastructure. A focus on CCB would need to foster growth of resources available to territorial governments and local communities, hardening the region’s cyberspace and support incident response to malicious cyber actor activity. Information technology security (ITSEC) resources need to be combined with community-based media literacy and critical thinking education programs to increase the region’s resilience to malign influence.
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 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".