A typology of “infrastructure of the middle” in university food procurement in England and Canada:
Bibliographic record
Abstract
This article introduces a new term – “infrastructure of the middle” – and explains how it helps understand how sustainability transition will happen in the food system. The evidence comes from 67 interviews with leaders of university food procurement initiatives in England and Canada. As founder and former president of the civil society organization which played a central role in the Canadian example, I bring a perspective informed by praxis, both as a practitioner and as a scholar applying Sustainability Transition Theory. I adapted the term infrastructure of the middle from Kirschenmann et al.’s concept of “agriculture of the middle”, which describes the midsize farms and ranches most at risk in a globalized food system. Infrastructure of the middle refers to the resources and networks that create a critical mass, enabling mid-size sustainable food producers to meet the needs of foodservice clients, especially public sector institutions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.022 | 0.023 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".