The benefits and burdens of care: A gendered analysis of American elementary school teachers navigating uncertainty at the onset of the COVID-19 pandemic
Bibliographic record
Abstract
This paper brings together anthropology of uncertainty and gender and education to examine the gendered daily experiences of elementary level teachers in the United States at the onset of the COVID-19 emergency. We found that teachers responded to ongoing uncertainty through various forms of care both at work and home: care for students and their families, their colleagues, their school community, their own families, and when possible, themselves. We argue that this care work served as a key mechanism that teachers used to navigate the uncertainty posed by the pandemic while simultaneously serving as a weight that exacerbated their stress, anxiety, and workload, and ultimately limited their capacity to care for themselves. Additionally, we argue that the care-laden responses of elementary school teachers to this crisis both reflect and reify the particularly gendered ways that women are tasked with the necessary work of nurturing in schools as well as in families. This work makes a theoretical contribution to the literature on education in emergencies by framing the concept of emergency through the lenses of uncertainty and gender. We show how providing education in emergency settings can be a productive process functioning along relational and temporal axes. Furthermore, we shed light on the day-to-day work of teachers in a global health emergency and provide a framework for understanding the immense and often gendered care work they do. Finally, by situating this article in the United States, we seek to highlight the presence of emergencies across the Global North, thereby making a case for extending the concept of emergency within the field of education in emergencies beyond the Global South. By examining early pandemic patterns of intensive care work conducted by teachers, this paper helps to explain the global crisis of teacher burnout and attrition 2 years after the pandemic began and offers insight for those seeking to prevent teacher burnout in the next emergency.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".