The New Governance of Sustainable Food Systems: Shared Insights from Four Rural Communities in Canada and the EU
Bibliographic record
Abstract
For over half a century, industrial agricultural and food systems have developed to the detriment of rural spaces.Alongside modernization and growth, many local communities have experienced not only economic loss, but a loss of purpose and identity as well.As one response to these changes, sustainable local food systems (SLFS) initiatives are being pursued by a growing number of communities.Their belief is that an alternative paradigm based on SLFSs is needed to support vibrant rural livelihoods: by challenging unequal power relationships between food system actors, by repairing the rift between human and natural environments, or simply by breathing new socio-economic life into their declining communities.This dissertation explores the governance mechanisms being developed between civil society, the state, and private sector actors to support SLFS initiatives.It aims to show not only what initiatives are developed, but how these alternatives are introduced and sustained.Building on governance theory and drawing from critical political economy approaches, this work argues that collaborative and reflexive governance approaches are best positioned to enable SLFS development.To support this claim, I describe and analyze cases of SLFS initiatives pursued within four rural communities: Todmorden, UK, Wolfville, Nova Scotia and North Saanich, British Columbia in Canada, and Correns, France.These case studies highlight six categories of governance that ultimately demonstrate low to highly collaborative and iii reflexive SLFS initiatives.Outlining types of governance and how they play out in practice allows us to better understand the opportunities and challenges inherent to different governance strategies and their ability to support SLFSs.Grounded in both field observations and in-depth and semi-structured interviews with community members, this work also aims to give voice to actors often marginalized in dominant food system processes.An analysis of the case studies highlights the need for 1) strong social capital within a community; 2) a whole community approach to socio-economic development; 3) a strong role for the state; and 4) genuine multi-actor collaboration, as the foundation for SLFS growth.I conclude by considering sustainable food system research's lingering question on growth and scalability to generate meaningful food system change.I never understood adding such lengthy acknowledgements to a PhD dissertation, until I realized the confidence and determination required to complete one.I feel such a great depth of gratitude towards those who helped me through this 6+ year process.Thank you, to my family for your endless support.Papa, for your boundless optimism and for instilling in me the belief in the power of democratic engagement.Mom, for telling me to align my work with my passions and interests.To my sister for always cheering me on, for picking up my late night post-thesis writing calls, and (subconsciously) motivating me to take on research that has real-world application.And thank you to each of you for altering your food habits based on all the things I have learned!Thank you to Andrew for always believing in me and telling me so frequently, for just making sure I could function, like reminding me to eat
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.034 | 0.018 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".