Foodwork in the onset of the COVID‐19 pandemic: The emotional experience among upper‐ and middle‐class women in Brazil
Bibliographic record
Abstract
Abstract Previous studies have shown that middle‐ and upper‐class, primarily white, women can relieve their workload and resolve family conflicts by relying on the labor of poor and/or racialized women or accessing services that facilitate their foodwork. However, the spreading of COVID‐19 and the necessity of social distancing have temporarily made the access of these facilitators difficult or impossible. Since women have been disproportionately affected by the pandemic consequences on the sexual division of labor, this paper examines how the pandemic affects women's emotional experience with domestic foodwork in Brazil. Drawing from the 588 upper‐ and middle‐class women's responses to an online survey, we have identified six emotional experiences influenced by the pandemic: (1) obligation, (2) overload, (3) fear, (4) safety, (5) relaxation, and (6) family time appreciation. However, the changes caused by the sanitary crises do not explain alone the new emotions experienced with domestic foodwork. Class and gender can interfere or potentialize how women feel about it during the pandemic. Obligation, overload, and fear were enhanced when the participants could not access services that were used to relieve their foodwork burden, especially when faced with an unequal sexual division of labor. In turn, safety, relaxation, and family time appreciation were facilitated by a better dynamic of domestic tasks sharing alongside the certainty to access good quality food. By analyzing these factors, this paper enhances the theoretical understanding of contextual and situational domestic foodwork emotional experience because it observes the outcomes of critical reduction of networks that used to sustain this practice involvement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".