Gendered Labor Continuum: Immigrant Mothers Confronting Uncertainty and Pandemic Constraints
Bibliographic record
Abstract
The literature on migration shows that legal status in receiving countries shapes immigrant experiences. While these studies effectively address the impact of precarious legal statuses on immigrant experiences, they often examine women’s labor in public and private spheres separately. Yet, women’s lives have long involved a continuum of paid and unpaid labor. The COVID-19 pandemic brought this continuum into sharp focus by spotlighting the influence of home and work dynamics. This study explores how immigrant women’s labor in both public and private spheres are interconnected. Drawing on 18 initial interviews with Venezuelan mothers in NYC from 2020, and 13 follow-up interviews in 2024, we examine the impacts of structural forces on these women’s labor arrangements and their strategies to navigate these impacts during and after the pandemic. Our findings reveal that while pandemic restrictions disrupted traditional labor market dynamics, they simultaneously intensified women’s engagement in domestic roles. Despite this, the mothers exercised agency by exiting the labor market and engaging in patriarchal bargaining at home. Post-pandemic, they lost access to the coping strategy, and their improved legal status did little to alleviate their labor struggles. This study highlights the significance of a “gendered labor continuum” in contexts that lack institutional support and undervalue immigrant women’s labor.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".