Public Funding for AI in Canada 2011-2022: An equity-focused environmental scan
Bibliographic record
Abstract
Abstract Background In this new equity-driven landscape, if there is to be system-wide transformation in research funding allocation, indicators of funding allocations need to be explored. This environmental scan aims to understand how research funding for artificial intelligence (AI) has been allocated and distributed in Canada from 2011-2022. Using geographical representations, we describe and map research funding for Canadian researchers by publicly funded granting agencies and provide analyses for AI research spending since 2011 Methodology We developed a rapid environmental scan to create a database of all AI funded projects from the following agencies: CIHR, NSERC, SSHRC, CRC, AMS, NFRF and CFI. Using publicly available research funding reporting and agency websites, we identified through title, keyword and project summary screening, AI projects in English and French for the years 2011-2022. Principal Findings A total of 4112 projects were identified, with the following information for each project recorded: title, year, institution, city, province, total funding, language, funding agency, funding program and primary investigator. A total of $384,933,265.74 million was allocated for publicly funded AI related projects in Canada from 2011-2022. Average funding per project was $93,612.18. The top three provinces with the most funding for all years are Ontario, Quebec, and British Columbia. The top three funding agencies by total amount for all years were NSERC at $155,267,817, CIHR at $136,594,644, and CFI at $58,317,627 Conclusion This information can assist in accountability and understanding of Canada’s publicly funded research allocations, and provide information related to the distribution of such funds, thus informing equity policy strategies.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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 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".