What do Farmers Think about Food Value Chains?: A Phenomenological Study of Farmers in Southern Ontario
Bibliographic record
Abstract
Many claim the industrial food system has complicated the supply chain, adding a large variety of actors, such as wholesalers and distributors that result in weaker local food systems. Direct marketing has often been promoted as the best alternative. Although direct marketing is a good option for some, many farmers find it cumbersome as it is time consuming and drains resources in return for profits that are not guaranteed. Food value chains (FVCs) provide farmers with a third option. FVCs are a potential response to the increasing demand for differentiated food including food that is sourced locally, food that provides fair compensation to farmers and promotes environmental and social improvements. FVCs have been steadily increasing in Southern Ontario. Online distribution channels, food box programs, online meal kits, small and alternative retails and mobile markets have sprouted to meet the demand of local food while handling high volumes of quality food and aiming to build local food systems. Although some research has been done to demonstrate the benefits of FVCs on consumer satisfaction and on farmers economically, little qualitative research examining the motivations, opportunities and challenges of farmers participating in FVCs have been recorded. Utilizing phenomenology, in-depth interviews with farmers will identify reasons for participating in FVCs, their perceptions of opportunities and challenges as well as their opinions about FVCs generating social and environmental benefits of developing strong local food systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".