Peeling Back the Layers: Prototyping Systemic Transformation through the Circular Food Innovation Lab
Bibliographic record
Abstract
Wasted food — the result of a linear pattern of producing, under-consuming and disposing of food — is a pervasive issue globally and in Canada. Wasted food is a complex challenge, meaning it is characterized by unpredictability, ambiguity, and many actors. The current climate crisis, food insecurity, economic disparity and housing inequality all intersect with this challenge. If we are to tackle these increasingly complex issues in social and public sectors, we need to work together in new and emergent ways. The Circular Food Innovation Lab was a unique research initiative that drew together municipal government, interdisciplinary designers and regional food businesses – grocers, food producers, distributors, restaurants and vendors – to tackle these complex challenges through systemic and service design methodologies, asking “how might we work together to increase circularity in Vancouver’s food system so that food is not lost or wasted; access to food is nourishing, equitable, and culturally appropriate; and habitats are protected for current and future generations of humans and more-than-humans?”
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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".