When the Teeth Eat the Tail: Defence AI in Canada
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 Canada is in trouble when it comes to defence artificial intelligence (AI) and is positioned to become a cautionary tale of the early AI years. Although Canada is well-placed globally for AI research, development, funding, and implementation, the country’s defence force is badly positioned to embrace digital transformation. This is a consequence of the organization’s structure, history, and culture, rather than of technical shortcomings. Without remedy, Canada’s AI systems will be small-scale projects, spread throughout siloes within the military complex, with almost no cross-pollination between them. These AI systems will be focused on hyper-specific operational and tactical uses cases faced by the various commands. Currently, Canada focuses primarily on data analytics, intelligence, surveillance, and reconnaissance, mine clearing, targeting and medical services. Defence AI research is supported by significant government funding. However, the Canadian Armed Forces face an uphill struggle in their attempts to both recruit new talent as well as make proper use of the existing talent within the armed forces in defence AI.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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