Essential workers and the cultural politics of appreciation: sonic, visual and mediated geographies of public gratitude in the time of COVID-19
Bibliographic record
Abstract
What do sonic, visual, and mediated forms of public gratitude for essential workers during the COVID-19 pandemic tell us about the cultural politics of the category “essential worker”? What racial, gender, and class structures and processes shape the content, form, and composition of these collective practices of appreciation? In this paper, I draw on my participation in a nightly ritual of collective applause in Vancouver, Canada, my encounters with homemade banners in my neighborhood, and my own familial histories and relationships to essential workers to examine cultural practices of gratitude. I observe that collective and public expressions of gratitude are shaped by existing structures and discourses of material inequalities, which manifest in hierarchical valuations of differently positioned essential workers. Cultural practices of gratitude, I show, can inadvertently serve to obscure the existence and continuation of these hierarchies by flattening or narrowly circumscribing who counts as an “essential worker” during the COVID-19 pandemic and, thus, limiting just whom public and collective expressions of appreciation are for. As cultural geographical doings, such landscape markers and spatial performances of gratitude serve to emplace and, thus, reinforce existing social hierarchies, suggesting the need for other more socially just spatial enactments of gratitude.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".