Establishing Priorities for Organic Research in Canada
Bibliographic record
Abstract
In a world of many competing interests for limited funds supporting research for agriculture and food, organic sector stakeholders and funding program managers are faced with a tremendous challenge in defining research priorities. The organic sector encompasses all aspects of agriculture including crops (fruits, vegetables, grains, pulses, oilseeds, etc.), livestock (production and welfare of dairy, poultry, beef, pork), not to mention food (storage, handling, processing, packaging, additives), and of course environmental issues (nutrient loading or depletion, sustainability, greenhouse gas emissions, energy intensity, soil conservation, biodiversity) and economic/ marketing issues (consumer/market demand, cost of production, profitability). In each of these areas, the growth and development of the organic sector will depend on its ability to capture existing opportunities, create opportunities through innovation, 2.1 Introduction ......................................................................................................7 2.2 Identifying Strategic Research Areas: Considerations .....................................8 2.3 Strategic Priority Planning Process for Organic Research Objectives ........... 10 2.4 Macroenvironmental Scan .............................................................................. 10 2.4.1 Social .................................................................................................. 10 2.4.2 Technological ...................................................................................... 11 2.4.3 Economic ............................................................................................ 13 2.4.4 Environmental .................................................................................... 13 2.4.5 Political/Regulatory ............................................................................ 14 2.5 Prioritization Process ...................................................................................... 15 2.6 Methods for Determining Science Categories, Impact Criteria, and Weighting ................................................................................................. 15 2.7 Outcomes ........................................................................................................ 18 2.8 Conclusions .....................................................................................................30 References ................................................................................................................ 31 maintain or improve competitiveness, and address barriers. So how do we set priorities for organic research? This chapter will outline the process undertaken to establish research priorities for the organic sector in Canada.
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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 | 0.043 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.026 | 0.011 |
| Scholarly communication | 0.029 | 0.006 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 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".