Bibliographic record
Abstract
Agricultural research has been a very important factor in enhancing the productivity and competitiveness of the Canadian agri-food sector. Science and innovation have been identified as together forming one of the five pillars in the Canadian Agri-food Policy Framework (APF). A major focus of the science and innovation section of the APF is to plan to realign public and private research efforts into a more comprehensive strategic approach for research and innovation in Canada. Despite the importance of research and the need for a strategic approach, however, assessment of critical strategic research policy issues for the Canadian agri-food sector has been limited. Implications and Conclusions This article addresses a variety of strategic policy issues facing the agricultural research establishment in Canada. First, the returns to public agricultural research are examined to show that agricultural research typically generates very high returns and is a very good investment of public funds. The distribution of benefits between producers and consumers is then examined to show that most of the benefits of public agricultural research in Canada go to producers, making it one of our most cost effective policies for improving farm incomes. Next, changes in agri-food research and technology transfer capacity in Canada are assessed; significant declines in the level of public sector research effort in recent years and the potential impacts on future competitiveness are documented. Finally,
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.260 | 0.100 |
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".