Governing evidence use in the nutrition policy process: evidence and lessons from the 2020 Canada food guide
Bibliographic record
Abstract
Nutrition guideline development is traditionally seen as a mechanism by which evidence is used to inform policy decisions. However, applying evidence in policy is a decidedly complex and politically embedded process, with no single universally agreed-upon body of evidence on which to base decisions, and multiple social concerns to address. Rather than simply calling for "evidence-based policy," an alternative is to look at the governing features of the evidence use system and reflect on what constitutes improved evidence use from a range of explicitly identified normative concerns. This study evaluated the use of evidence within the Canada Food Guide policy process by applying concepts of the "good governance of evidence" - an approach that incorporates multiple normative principles of scientific and democratic best practice to consider the structure and functioning of evidence advisory systems. The findings indicated that institutionalizing a process for evidence use grounded in democratic and scientific principles can improve evidence use in nutrition policy making.
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 | 0.484 | 0.627 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.026 | 0.012 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 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".