Seizing this COVID moment: What can Food Justice learn from Disability Justice?
Bibliographic record
Abstract
It is now a shameful truism that COVID-19 functioned as a big reveal, exposing, and amplifying the structural inequalities Canadian society is built upon. We are now a year and a half into the global pandemic. I am writing from Toronto, where “hot spots” (neighbourhoods with high infection rates) is code for racial and economic inequality (Wallace 2021) and public health guidelines have rendered low income “essential workers” disposable, amidst ballooning food insecurity rates, especially in low-income racialized communities (Toronto Foundation 2020; CBC News 2020). We are all in the same storm but in very different boats, as the new saying goes. I want to suggest that this moment, as Canadians are poised to step out of lockdown and return to ‘normal’, is a particularly useful one for Food Studies to consider what we could learn from Disability Justice movements in order to address a glaring hole in our collective scholarship and analysis.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.034 | 0.068 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".