The Food For Life Catering Mark: Implementing the Sustainability Transition in University Food Procurement
Bibliographic record
Abstract
This article presents a case study of the application of the Soil Association’s Food For Life Catering Mark at two universities in England: Nottingham Trent University and University of the Arts London. This procurement initiative has had noteworthy success in the U.K., with more than 1.6 million Catering Mark meals served each weekday. This article, based on 31 in-depth interviews conducted in 2015, is the first to examine its impact and significance at the university level. In particular, this article tests the concepts of the niche, regime and landscape in the multi-level perspective (MLP), a prominent theoretical approach to sustainability transition, against the experience of the Food For Life Catering Mark. The article confirms the importance of the landscape level of the MLP in the food sustainability transition, while adding additional considerations that need to be specified when applying the MLP to the food sector. By highlighting the essential role of civil society organizations (CSOs), public institutions and many champions, this article proposes that more room must be made within the MLP for the explicit role of agency, champions and the implementation process itself. Indeed, this article argues that implementation, the daily practice, is deserving of both increased recognition and theory.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".