practices for Canadian agricultural innovation: lessons from theory and practice. Report prepared for Agriculture and Agri-Food
Bibliographic record
Abstract
Historically, agricultural innovation has been a very important source of economic growth in Canada. Innovation in genetics, products, practices, processes, and institutions have allowed the sector to increase both the quantity and quality of products available to consumers, while freeing up labour, land and other resources for use elsewhere in the economy. Despite this strong record of innovation, there is growing consensus of a critical need to improve policies in support of agricultural innovation in Canada. Slowing rates of productivity growth, underinvestment in research, and poor records of value added commercialization suggest that government innovation policies have become less effective over time. At the same time, the growing global demand for basic food, bioproducts, and functional nutrients, suggests increased opportunities for innovation. In an increasingly globalized economic environment, remaining competitive is not only financially rewarding, it is essential to the survival of this vital sector. This paper provides an overview of current theory regarding the importance of innovation to the agricultural sector’s competitiveness, describes key factors that influence the rate
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".