Food Counts: Food systems report cards, food sovereignty and the politics of indicators
Bibliographic record
Abstract
The International Panel of Experts on Sustainable Food Systems recognized that "current systems will be held in place insofar as these systems continue to be measured in terms of what industrial agriculture is designed to deliver, at the expense of many other outcomes that really matter in food systems" (IPES 2016: 57). In response, they called for new food systems indicators rooted in social justice, support for local economies, ecological regeneration and democratic engagement. This paper reflects on the ways that indicators can serve as a tool to understand the current state of food systems, challenge existing approaches and (re)frame a future vision of equity and sustainability. Our analysis focuses on the development of Food Counts: A Pan-Canadian Sustainable Food Systems Report Card, a first attempt to bring together existing measures of social, environmental, and economic well-being to help researchers, policy makers, and practitioners examine food systems more comprehensively. The report card used a food sovereignty framework and an integrated systems perspective and make connections to a global movement for collective social change. Beyond its practical value, and particularly in the context of Canada's development of a national food policy, our analysis illuminates the limited kinds of data available, the privileging of scientific expertise over traditional knowledge, the assumed value of certain indicators, and the reductionist nature of using data to represent complex food systems. We argue that while report cards can make visible numerous food systems' elements, they can also obscure divers experiences, reinforcing unsustainable practices and policies.
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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 | 0.022 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.030 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.024 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".