Fallow Fields or Fertile Ground: Analysing Food Governance in a Changing World
Bibliographic record
Abstract
Our food systems are failing us. In particular, industrial food systems fail to deliver healthy, adequate food to billions of people (FAO et al. 2019; FAO et al. 2024) and have led to increased instances of mental health crises for farmers (Jones-Bitton 2019; Younker & Radunovich 2021). In addition, industrial food systems contribute to the devastating impacts of a warming climate (IPCC 2021), biodiversity loss (WWF 2021), and soil erosion (Wise 2019; Badreldin and Lobb 2023) worldwide. Yet, little structural change has occurred in the way agriculture is governed and the problems created by industrial agriculture are becoming increasingly entrenched. Given this context, the research in this dissertation asks: Why, in an era of converging crises, has agricultural policy remained largely unchanged and supportive of industrial agriculture practices? The research included in this dissertation is underpinned by current academic literature that constructs my understanding of food systems challenges and changes in global governance over the past decades (Chapter 2). Presented as three research papers (Chapters 4, 5, and 6), each chapter focuses on a unique aspect of governance and is enabled by the different methods laid out in Chapter 3. Chapter 4 lays out the methods and methodology used for the three research papers (Ch. 4, 5, and 6) included in this dissertation. Chapter 4 focuses on actor arrangements and power in Canadian federal agricultural policy while Chapter 5 focuses on how different food systems actors experience multistakeholder governance arrangements. Different examples of how place-based, rights-centred governance arrangements can offer a more equitable outlook for food systems are assessed in chapter 6. The concluding chapter (7) provides a set of tools and strategies to help bridge the gap between the findings of Chapters 4 and 5 with the examples of transition in Chapter 6.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".