Bibliographic record
Abstract
In recent years, resilient food systems have become a policy priority for municipal governments, especially given concerns about climate change, the impacts of COVID-19, and rising food insecurity in Canada. The term resilience is often used to describe the ability of individuals, communities, nations and systems to recover from disruptions. However, resilience is frequently employed within policy discourse without clear definition or communication as to who or what should be resilient. The ambiguous use of the term can lead to inadequate policy and often fails to address systemic issues that create food system and social inequities in municipalities. Our analysis examines how the City of Toronto has framed resilience within food system policy discussions and compares these framings with the perceptions of resilience held by local community-based food system actors. Through an analysis of sixteen (n=16) municipal documents and twenty-eight (n=28) key informant interviews, our findings suggest that the rhetoric of resilience has little actual influence on food policy. Instead, it is often used to describe an idealized food system and indirectly places the responsibility on individuals to be resilient amid ongoing and multifaceted crises. The study contributes to critical discussions on resilience in food systems literature, arguing that resilience often reinforces a neoliberal mindset that prioritizes economic system resilience over the well-being of populations. The momentum towards community-driven, culturally responsive, localized food initiatives in Toronto is a positive step. However, we suggest that food system scholars, practitioners and policymakers engage with the concepts of ‘resilience’ more critically and with more intention, being mindful of the systems of oppression and exploitation inherent to the concept.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.068 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".