community food centre: Using relational spaces to transform deep stories and shift public will
Bibliographic record
Abstract
COVID-19 has revealed deep inequities in our food system. As goodwill and charity from this crisis disappears, and emergency supports begin to dwindle, we can anticipate increased food insecurity amongst Canadians. Rising food prices and unemployment will drive a lack of access to fresh nutritious foods for already stressed and vulnerable individuals. As a community organizer who has advocated for poverty reduction and food justice over my lifetime, I understand the short-lived nature of change that occurs without public will and engagement - policy wins end up being removed in the next election cycle. My experience with party-dependent advocacy projects has led me to ask the question: how do we build the kind of public will that demands access to healthy and nutritious food as not an individual responsibility but a public duty, much like universal healthcare? In writing this paper I intend to draw upon my experiences in organizing to explore the deeper cultural and internal shifts that may need to occur to inspire public will and create change that lasts beyond a single election cycle, and the opportunity that COVID-19 presents as Canadians grapple with questions about food security and poverty in an unprecedented time. I will connect with three community members I advocated with in my time doing placebased community organizing, all with different experiences of food insecurity, and use a storytelling approach to imagine a more effective way of advocating for just food futures.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.053 | 0.057 |
| Scholarly communication | 0.024 | 0.012 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 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".