‘Good morning Metro shoppers!’ Food insecurity, COVID-19 and the emergence of roll-call neoliberalism
Bibliographic record
Abstract
From April 2020 to December 2021, the Canadian federal government earmarked $330,000,000 through the Emergency Food Security Fund to address food insecurity during the COVID-19 global pandemic. These funds were disbursed through a handful of national and regional emergency food and food justice agencies to smaller front-line organizations for the purchase of emergency food provisions and personal protective equipment, and to hire additional workers. We theorize these dynamics within the broader processes of neoliberalization and argue that the Canadian federal government was conscripting food justice and community development organizations into its efforts to address dramatically increasing rates of food insecurity across the country through charity emergency food provisioning. Within Peck and Tickell's stylized conceptions of the destructive (roll-back) and creative (roll-out) moments of the process of neoliberalization, we frame the crisis of COVID-19 as exposing a form of recalibration (roll-call) neoliberalism. We focus on this dynamic specifically within the context of household food insecurity in Canadian communities and argue that the federal government's funding regime during the global pandemic effectively directed food justice organizations (and by extension, the populace in general) away from a more ambitious social change agenda towards the more acceptable strategy (in neoliberal terms) of emergency food provisioning services.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.040 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".