A Case for Reinforcing Agri-food Research and Development Spending: Where Does Canada Stand Internationally?
Bibliographic record
Abstract
Global spending on agricultural research and development (R&D) increased from $31 billion in 2000 to $47 billion in 2016, reflecting the sector's growing importance for food security, climate adaptation, and economic competitiveness (IFPRI, 2020). Despite these global advances, Canada’s agricultural R&D spending declined from $0.86 billion in 2013 to $0.68 billion in 2022, ranking it lowest among the top seven OECD countries (OECD, 2022). Countries like China and Brazil demonstrate how strategic investments can drive innovation, sustainability, and economic resilience. This policy brief analyzes global trends, highlights Canada’s comparative underperformance, and offers actionable recommendations to strengthen its agricultural R&D framework. Prioritizing increased investment, fostering public-private partnerships, and integrating sustainability into research agendas are critical for ensuring Canada’s agricultural sector remains competitive and resilient in the face of global challenges.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".