Analysis of the Organic Beef Markets in Ontario and Alberta: A Case Studies Comparison
Bibliographic record
Abstract
Consumers are increasingly interested in paying a higher premium for alternative food, such as natural and organic products and governments are increasingly interested in sustainable agricultural practises that preserve the ability of future generations to produce food. The organic movement is continually growing due to consumer health and environmental concerns. This study focuses primarily on the evolution of the organic beef sector in Canada, focusing on two case studies: the MD of Pincher Creek No. 9 in Alberta and Frontenac County in Ontario. By interviewing stakeholders in the organic beef industry in both regions and supporting the stakeholder accounts with a literature review, comparisons were made pertaining to the viable market options that each region faces. Alberta is one of the leading producers of organic beef in the country, while Ontario produces much less, but Alberta has a lower level of organic consumers than Ontario. This leads to the need for Alberta producers to market their beef elsewhere, in British Columbia and the United States. Frontenac County has much more access to urban markets with high levels of organic consumer activity and thus have an easier time logistically in the selling of their beef. These two cases represent contrasting market and policy needs though regulation and certification requirements are nation-wide.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".