Priorities, Narratives, and Collaboration: Insights From Evolving Federal Mandates on Food Systems in Canada
Bibliographic record
Abstract
Whether it is in a post-election period, a cabinet shuffle, or prorogation of parliament, the speech from the throne and mandate letters signal a government's priorities as they relate to emergent issues and long-standing public policy challenges. While the speech from the throne has been regularly available through parliamentary and government records, federal mandate letters have only been made publicly available more recently, and little research has been done on their role in shaping change. Using Critical Discourse Analysis (CDA), the authors explore how the overarching narratives presented by the current federal government have evolved across the period from 2015 to 2021. The authors then compare these narratives with the mandated commitments to the Minister of Agriculture and Agri-Food Canada (AAFC) during the same period. Through this comparative analysis, the authors highlight how the overarching narratives that emerged in later mandates, in particular the need to address systemic inequity, diverge with the commitments delivered to the Minister of AAFC. Part of the reason for identifying the divergence between central narratives and the current AAFC mandate is the hope that better alignment is possible. This includes making a new food policy environment in Canada; One that is equitable, prosperous for all, supports true reconciliation and Indigenous sovereignty, and ushers in a brighter future for the next generation and our planet. To conclude, the authors present alternative food systems frameworks that could help better achieve the more just and resilient world that the federal government narratives outlines.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.063 | 0.032 |
| Scholarly communication | 0.023 | 0.008 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".