Creating a local food procurement community of practice: The Alberta Flavour Learning Lab
Bibliographic record
Abstract
In order for local food initiatives (LFIs) to have a transformative effect on the larger food system, greater levels of economic, organizational and physical scale are needed. One way for LFIs to reach the scale necessary to generate a more significant impact is through increased institutional procurement of local foods. But how do people and organizations come together to generate the social infrastructure required to shift food purchasing practices and processes? This field report shares the story of an innovative community of practice consisting of institutional food buyers, large-scale distributors, regional retailers, processors, producers, researchers, municipal and provincial government representatives within the Edmonton city-region that formed for the express purpose of “creating a positive community impact by getting more local foods on more local plates”. In describing the formation and first three years of the Alberta Flavour Learning Lab we examine the unique characteristics of this community of practice that has aided the development of a common framework for learning, understanding and joint action. In addition to the accomplishments to date, we also discuss the challenges faced by the Learning Lab and the strategies used to overcome them.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".