Local Food, Scale and Conventionalization: Mid-scale Farms and the Governance of âLocal Beefâ Chains
Bibliographic record
Abstract
Media and consumer attention has propelled local food to prominence, and a significant price premium has signaled its potential as a value-added option for family farms looking to transition from commodity production. Many of these farms –entering an unfamiliar market– have been selling local food in groups, to share risks and investments. This strategy has introduced a scale of production and operations to the marketplace that could challenge some of the basic premises of the local food contract.\nThis research project was premised on the notion that the local food movement –dominated by small-scale production and direct marketing– appears to be governed by a set of principles that would be tested by the introduction of farms and farm groups of increasing scale. To understand the implications –for these farm families, local food marketing groups, and growing local food systems– this research sought to address whether these groups would adopt a more conventional approach to meet their needs, and fit their scale, or change their approach and practices to conform to the requirements and expectations established by the principles of governance that characterize local food systems.\n‘Local beef’ chains from across Ontario were selected to capture a range of operational and geographic scale. Interviews with farmers and coordinators investigated the extent to which scale –at farm and group level– affected motivations, as well as group governance decisions. \nThe research found that increased group scale limits the range of options available, and magnifies pressures towards conventionalization. Transition to larger scale favours governance based on surveillance and discipline, and suffers from lack of infrastructure that would facilitate mid-scale aggregation, distribution, and the development of bridging capital.\nFarmers who had direct input into decisions invested more time and effort, but also identified more strongly with their group. Farmers who marketed through larger-scale intermediary-led groups faced fewer costs, but were treated as commodity input suppliers, and were less engaged in the group’s success. Most farmers did not see these intermediary-led groups as a long-term solution, and looked instead to policy solutions, or other alternative marketing models – including smaller-scale regional intermediaries.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".