NPR - 2020: Planning For The Business Management Needs Of Canadian Farmers - When You Don’t Know What You Don’t Know
Bibliographic record
Abstract
While the world calls upon farmers for increased productivity, farmers face intensifying volatility from the marketplace, weather and in consumer trends and must manage the social, economic and environmental impacts of farming like never before. As agriculture continues to prioritize production management over business management, farmers will continue to struggle against an increasingly volatile and complex sector. Furthermore, with a reduction in Government programming to manage risk, now more than ever, Canadian farmers will have to rely on their business management skills to not only stay in business, but to succeed. In an ever-changing and complex industry, business management provides a solid foothold for farmers to confront change with confidence, manage risk, seize opportunity and make informed decisions. This signals an opportunity to improve the awareness and adoption of beneficial management practices, and further, to demonstrate the tangible results of adopting beneficial management practices. Indeed, success will be increasingly reliant on the business management skills of the farmer. Farm Management Canada is the only national organization dedicated exclusively to the development and distribution of business management information to Canadian farmers. In fulfilling its mandate to increase farmers’ awareness and adoption of beneficial management practices towards the realization of business goals, FMC must be in tune with both the learning preferences and practices of farmers to meet their learning needs with not only the information they want, when they want it, and how they want it, but also the information they need. This paper focuses on a report commissioned by Farm Management Canada titled 2020: Planning for the Business Management Needs of Canadian Farmers and Farm Management Canada’s efforts to meet these needs through diverse, multi-faceted and multi-medium knowledge transfer programming.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".