Intersectional Feminism, Racial Capitalism, and the COVID-19 Pandemic
Bibliographic record
Abstract
A common narrative during the COVID-19 pandemic correctly observed that lockdowns which stripped public interactions to the bare minimum exposed the economic and social fault lines of Canadian society. Since March 2020, women— particularly racialized women in jobs with low pay—have disproportionately borne the brunt of the pandemic’s negative economic impacts. The fact that racialized women face systemic economic inequality and marginalization in Canada is not new. It is the structural foundation of an economy and sex-segregated labour market rooted in racial capitalism. The pandemic merely made the disparities impossible to ignore. At the same time, a reckoning with the roots of structural inequality has become ever more pressing because, coincident with the pandemic, mass politicization and mobilization accelerated on a global scale in response to police killings of Black people, state violence against Indigenous land defenders, rising fascist movements, anti-Asian violence, and the climate emergency. Intertwined social solidarity movements have forged deeper connections in the heat of these collective traumas. And the pandemic-induced period of reflection and questioning brings urgency to widespread demands for deep social and economic transformation. These movements demand that we confront who the imagined “we” is in the mantra that “we're all in this together” and who benefits from that depoliticized narrative framing.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.049 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".