Profitability and organic farming in the province of Ontario
Bibliographic record
Abstract
This research intends to identify the fundamentals of organic farms' profitability structure in the Province of Ontario with an interdisciplinary focus, which includes small and medium-sized farms that provide organic food directly and indirectly through intermediaries to consumers. Hence, organic farmers need to remain profitable to stay in their non-profit-driven businesses and contribute to environmental sustainability, socially responsible farming and achieve its prosocial and pro-environmental goals. I used a questionnaire; consisting of four sections: company or farm-related information, general socio-economic questions, financial information for threeyear periods (2015 – 2017), and environment and public health-related information containing open and closed-ended research questions. First, I sent a hard copy of the questions and then collaborated with the Organic Council of Ontario and used the SurveyMonkey platform to increase the number of participants. My findings show that organic farm products are critical to enhancing the health of Canadians. Additionally, organic farming plays a vital role in protecting the environment by reducing soil erosion and water pollution, among other benefits, thus being critical to national development. It also was established that the government should ensure that the assistance offered to organic farmers reaches each farm and develops financial instruments to support the farmers. My Ph.D. thesis provides researchers with an overview of the profitability of organic agriculture in Ontario to inform future studies. In addition, it offers policymakers vital information applicable in formulating regulations towards boosting organic farming in Canada. Government support likely plays a role in the success of a lower-cost sustainable organic food system. My study established that politicians should ensure that the assistance offered to organic farmers reaches each farm and develops financial instruments to support the farmers financially. Finally, this research will be useful for farmers considering organic food growth, policymakers trying to determine whether organic farmers require subsidies, and scholars who would like to know more about the profitability structure of organic farms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".