Farmers’ Markets as Social Economy Drivers of Local Food Systems
Bibliographic record
Abstract
The objectives of this project are as follows: 1. To examine the literature on FMs through a social economy lens: a) To examine and compare the role of FMs in the development of local food systems in different global contexts - e.g. North America, Europe, Asia, Latin America; b) To gather information on the history of FMs in Canada, particularly BC and AB contexts (ie. origins, locations, function, organizational structure) and what influences have shaped this. What is the distribution of public, social, and private investment in supporting the development of farmer’s markets in BC/AB (land, buildings, infrastructure, administration). What actors within the social economy are taking the lead in this area, and what role do FMs play in their larger objectives/initiatives? c) To identify themes emerging from the literature regarding the successes and challenges of FMs, and if possible to assess the extent to which FMs are (or could be) a driver of community food systems. 2. To develop individual case profiles (shortened version of a case study) of FMs clustered within a regional setting in BC and AB (no more than 10 FMs in each province) in order to evaluate and compare their current and potential role in advancing local food systems, individually and as part of an interacting regionally-based network. We are particularly interested in understanding if and how a regional cluster of FMs can stimulate short supply chain development. As part of a network analysis, we will investigate a number of relevant variables such as producer marketing mobility within a regional market cluster, competition for marketing space at different FMs, and FM relationships to other local businesses and community organizations. What purposes does the FM serve beyond sales – e.g., production and marketing knowledge exchange, political networking and advocacy, building awareness about local production and consumption? What is the potential for FMs to become focal points for warehousing, processing, and other forms of distribution of local food products, including public procurement? 3. To establish a Delphi method of inquiry in order to engage a group of experts (e.g., producers/vendors, FM managers, FM association representatives, academics, government representatives) in: 1) assessing the prospects for, and conditions affecting, FMs becoming a driver of the re-localization of food systems in BC and AB in the coming decade; and 2) proposing criteria for the success of FMs in this role in BC and AB. For further details on this method see attached appendix.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".