Small Farms, Big Impacts: A Case Study in the Development of a Sustainable Farming Livelihood for Direct-Marketing Farmers in Southwestern Ontario, Canada
Bibliographic record
Abstract
Direct-marketing farms play an important role in fostering healthy communities in an era of rapid climate change and unsustainable global agro-industrial practices. Canadian cities intimately depend on foodstuffs imported from countries most affected by climate change events which may cause food shortages and an increase in future food costs. At the same time, the use of heavy machinery and harsh chemicals employed on industrialized farms prevent long-term use of the land by degrading its fertility. In an effort to combat these deficiencies of the global agriculture system, direct-marketing farms have \nbecome increasingly prevalent and popular in Canada. Direct-marketing farms offer social benefits such as a sense of community and food education, as well as environmental benefits through sustainable farming techniques. Unfortunately, direct-marketing farmers are typically earning an income less than minimum wage and are therefore not able to support themselves, their families, and their businesses for the long-term. Without a feasible business model to foster a more sustainable livelihood, direct-market farming will never become widely adopted – despite its many benefits. \nIn turn, this thesis seeks to explore the most useful business strategies to be employed by \ndirect-marketing farmers to procure a more sustainable livelihood. First, a literature review was undertaken to ascertain the current opportunities and challenges of direct-market farming. Various ways of assessing wellbeing of direct-marketing farmers was then considered. Second, several farms in Southwestern Ontario were investigated in a case study approach using semi-structured interviews and participant observation to gain insight into the first-hand experiences of operating direct-marketing farms. The results of this thesis contribute to the literature by filling a gap pertaining to how one may generate a sustainable and economically viable livelihood as a direct-marketing farmer. Currently, the literature solely recognizes the significance of direct-marketing farms without detailed accounts of the ways in which such enterprises can be sustained. Also, by determining the requisite elements of a viable business model, the findings may encourage more people to initiate direct-marketing farms and proliferate the widespread and beneficial impacts of direct-market farming.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.025 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".