Organizational factors influencing the growth of Canada’s scientific and research potential in the field of artificial intelligence
Bibliographic record
Abstract
A comprehensive analysis of the Pan-Canadian Artificial Intelligence Strategy and its implementation measures aimed at the growth of Canada’s scientific and research potential in the field of artificial intelligence forms the foundation of this study. Canada’s selection as the subject of study is attributed to its distinction as a pioneering country to adopt a strategy of this nature, and proving its status through drafting the Artificial Intelligence and Data Act, known as AIDA. The authors have discerned and deliberated on the main organizational factors that have positioned Canada as one of the leading nations in artificial intelligence in accordance with AI country rankings. This article presents the components of the Pan-Canadian Strategy, encompassing principal tasks and areas, including the practical introduction of novel technologies due to second-phase commercialization. It outlines the key focus areas of Canada’s public policy, including research, development and retention of skilled professionals, and the creation of essential infrastructure. The article also consolidates some significant societal outcomes realized during its implementation while identifying current trends. The foundation and activities of national institutions are underscored as pivotal in fostering scientific and research potential, with special emphasis on the initiative to establish a new institute dedicated to the safety of artificial intelligence under the strong influence of AI Safety Summit at Bletchley Park. The authors identify the key participants in the artificial intelligence ecosystem who have the most influence on implementing the Strategy. The conclusions drawn from the article aid in fostering a deeper comprehension of the role played by organizational and administrative processes in propelling advancements in the field of artificial intelligence. The favorable impact on societal development is highlighted, provided risks are mitigated. Given Ukraine’s historical association with high intellectual potential, the findings of this study can be instrumental in honing the national policy pertaining to artificial intelligence in Ukraine.
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".