‘You're just invisible and you don't matter at all’: The structural violence of the COVID-19 Canada Emergency Response Benefit (CERB)
Bibliographic record
Abstract
In March of 2020, the Canadian government introduced the Canadian Emergency Response Benefit (CERB), a taxable income transfer of $2000 CAD a month received by millions of Canadians during the COVID-19 pandemic. We argue that CERB is an example of structural violence because the eligibility requirements excluded many Canadians living in poverty. CERB provided a window into the structure of power in Canada, differentiating those ‘deserving’ of government support (i.e., temporarily unemployed workers) from those who were not. We undertook this qualitative research to document the experiences and perspectives of individuals living in poverty who were ineligible for CERB. An analysis of 28 interviews shows that participants felt their suffering was invisible to policymakers, particularly in relation to unmet basic needs, including food and shelter; stigma and lack of dignity; and the ongoing stress of economic precarity. Participants were unanimous that CERB should become permanent and made available to all in poverty. Although millions of Canadians experienced a temporary form of basic income during a time of crisis, it is unclear how to mobilize that experience to pressure government to provide everyone with a permanent income floor sufficient to meet basic needs.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.030 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".