Hailing in the Face of Covid-19: On the Uses and Abuses of Heroism
Bibliographic record
Abstract
In this paper, we examine the paradoxes of hailing health care workers as “Covid-19 heroes” in Canada and the United Kingdom. We ask how public discourses—primarily by governments, politicians, mainstream media, but also by racially minoritized groups and migrant-led associations—frame the ambiguous social and legal status of mostly women of color “essential” health care workers during the pandemic. We argue that hailing is a form of conditional inclusion. Hailing involves both the camouflaging of individuals’ low-class status, precarious position in the workplace, gendered and racially minoritized positionality and insecure/non-permanent immigration status on the one hand, as well as the potential for resistance, emancipation, wider organizing, and claims-making on the other. Through a focus on Filipino/a workers because of their high levels of representation as health care staff in both contexts, our empirical analysis underlines that hailing as conditional inclusion is asymmetrical and unequal. It enables co-optation and deflection from structural inequalities as the price of conditional inclusion of selected individuals and groups. However, at the same time, hailing generates resistance. Through “tiny openings” these contradictions are named, and the binary language of inclusion/exclusion is challenged.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".