Advancing Intersectional Climate, Biodiversity, Food Justice & Sovereignty Principles : Evaluation & Comparative Analysis of Food Certifications
Bibliographic record
Abstract
As the University of British Columbia’s (UBC) food system emits over 29,000 tonnes of CO₂ every year, UBC’s Climate Action Plan 2030 (CAP 2030) has set a goal of reducing campus food system greenhouse gas (GHG) emissions by 50% before 2030 to improve campus sustainability (Campus and Community Planning, 2021). UBC can pursue food system sustainability through the use of food certifications, which help to provide transparency of the sustainability of food items. On top of improving campus sustainability, UBC’s interest in certifications may help influence other food suppliers and producers to look into food certifications as a method for sustainable food procurement due to their large purchasing power. The four core principles that this project aims to address are reducing GHGs, enhancing biodiversity, ensuring food justice, and promoting food sovereignty. Such principles can help UBC Food Services (UBCFS) further develop a Climate-Friendly Food System (CFFS) Procurement Strategy. The main objectives of this project were: (1) conducting a literature on different food certifications systems and their impacts on climate, biodiversity, food justice, and food sovereignty, (2) finding promising practices other institutions are including in their sustainable procurement policies that UBC can adopt and implement, (3) consulting local food system stakeholders to gain different viewpoints on food certifications, and (4) comparing and analyzing various food certifications to determine how each one could impact UBCFS’s sustainable procurement strategy for five core food categories (coffee and tea, produce, protein, dairy, and bread and baked goods). Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”
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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 | 0.128 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".