FarmWorks – providing community-based capital for farms and food
Bibliographic record
Abstract
Food production is fundamental to strong, rural communities and a key component of rural economic strength. Over the last half century, Atlantic Canada has witnessed a progressive erosion of food security, and an increased reliance on imported food. In December 2011, a small group of community leaders launched FarmWorks Investment Cooperative Ltd., a for-profit cooperative focused on providing strategic and responsible community investment in food production, processing, and distribution. Since 2011 FarmWorks has raised over $1 million in private investments under the umbrella of Nova Scotia’s Community Economic Development Investment Fund (CEDIF) program. FarmWorks is promoted to community members as an investment vehicle that keeps financial resources in the Region and supports the local agri-food economy, Investors benefit from significant tax rebates under CEDIF. In less than 4 years of operation, FarmWorks investments have had measurable success in supporting agri-food business start-ups and expansion, focusing on both market pull (restaurants, and processors featuring local products) and primary production (farms and food distributors). To date some 35 businesses have received FarmWorks loans, following rigorous review of business plans and financial viability. This presentation will describe the evolution of the cooperative and its impact on rural innovation and sustainability.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.007 |
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".