Public Trust as Infrastructure: Reframing the Role of Institutions in Canada’s Food and Rural Systems
Bibliographic record
Abstract
This presentation introduces the work of the Canadian Centre for Food Integrity (CCFI), a national nonprofit that conducts longitudinal public trust research and provides insights back to the food system. Drawing from its 2024 data and looking ahead to upcoming 2025 results, CCFI presents evidence that trust in Canada’s food system is declining across nearly all sectors—especially among younger Canadians—and that it cannot be rebuilt through communication alone. Instead, this session argues that trust must be treated as infrastructure: a measurable, strategic asset that underpins advisory services, policy implementation, and institutional resilience. The session will explore how trust-building can be embedded in rural and agri-food institutions, not as a marketing goal, but as a core function that guides engagement, accountability, and public value. The insights are based on one of Canada’s most comprehensive national studies of public perceptions related to food safety, regulation, animal welfare, sustainability, and innovation, with implications for rural organizations, extension agents, and institutional leaders working to serve a skeptical and rapidly changing public.
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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.042 | 0.047 |
| Scholarly communication | 0.027 | 0.015 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".