Visualising community-based food projects in Ontario
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In seeking to help address the question about what is distinctive about “alternative” food networks and “food hubs” in particular, this paper explores the strengths and limitations of using concept-mapping software to illustrate the organisational structures of community-based food projects in Ontario. As part of a larger research project, the authors developed concept maps that illustrate inputs, activities and assets, as well as different types of resources (public, private, citizen, etc.). This paper focuses on the benefits and challenges of choosing to share research results with the use of a visual tool, including the benefits of the process of our mapping exercise for the research team, for the research participants, and for dialogue among them all. Challenges include the difficulties of balancing nuance and uniformity, as well as complexity and simplicity, while visually representing networks that often blur the lines between governmental, public, non-profit, cooperative, multi-stakeholder and private.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it