Neoliberal growth vs food system democratization: narrative analysis of Canadian federal and civil society agri-food policy
Bibliographic record
Abstract
Narratives inform policymaking by building consensus, stabilizing our shared beliefs, and legitimizing our assumptions (Roe 1992, 1994). This research applies narrative policy analysis to identify and compare the dominant agriculture and food (agri-food) narratives of Canadian federal government and civil society policy over time. It aims to understand and compare what narratives are driving the agri-food policy priorities of each group, with particular attention to how policy narratives address social and environmental goals. This analysis documents and confirms a Neoliberal Techno-optimist Growth Narrative as the dominant narrative in federal Canadian Agriculture and Agri-food Canada (AAFC) policy between 1986 and 2019. Over a similar period, civil society has adopted narratives that prioritize localization and democratization of the food system as well as food security. While the neoliberal priorities of market expansion and competitiveness are the focus within federal narratives, civil society concerns related to the social and environmental costs of economic efficiency, including reduced farmer livelihoods, environmental degradation, and loss of community decision-making capacity, have received marginal attention from federal policy. We discuss how the Neoliberal Techno-optimist Growth Narrative imposes structural barriers on the pursuit of environmental and social goals by establishing a hierarchy of goals whereby environmental/social goals can only be pursued to the extent that they contribute to economic growth and by promoting a techno-optimist approach. As such, the dominant Neoliberal Techno-optimist Growth Narrative stabilizes two contested assumptions: (1) economic growth through liberalized trade is the best approach to achieve societal wellbeing, and (2) that technological innovation will sufficiently address environmental pressures. Supplementary Information: The online version contains supplementary material available at 10.1007/s10460-024-10647-3.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".