Exploring Food Hubs as a vehicle for building resilient local food systems
Bibliographic record
Abstract
Food systems around the world face unprecedented threats from climate change to global distribution and supply chain issues. At the local level challenges range from the accessibility and affordability of local food to limited capacity for production, aggregation, and distribution. These challenges can be particularly acute in rural, remote, and northern communities. In the face of these threats and challenges, alternative food networks are emerging as an opportunity to build more resilient, inclusive, and sustainable local food systems. Across British Columbia, community food hubs have emerged as one form of alternative food networks. This presentation will summarize the first year of a project exploring the role(s) that food hubs play within local food systems, investigating BC’s unique approach to food hubs and if and how food hubs contribute to food system resilience. Findings will include emerging patterns and trends from a provincial food hub community of practice, as well as an in-depth exploration of two case study regions - the Kootenays and Kamloops regions. While the research is taking place within British Columbia, the presentation will include food hub examples from other jurisdictions, and a discussion focused on potential applicability and transferability of findings to remote and northern communities.
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 | 0.003 | 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 | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".